Potential Source Density Function: A New Tool for Identifying Air Pollution Sources by In Sun Kim, Yong Pyo Kim, Daehyun Wee

“…Abstract Potential source density function (PSDF) is developed to identify, that is, locate and quantify, source areas of ambient trace species based on Gaussian process regression (GPR), a machine-learning technique. …”
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On Data Selection and Regularization for Underdetermined Vibro-Acoustic Source Identification by Laixu Jiang, Jingqiao Liu, Xin Jiang, Yuezhao Pang

“…The number of hologram points in near-field acoustical holography (NAH) for a vibro-acoustic system plays a vital role in conditioning the transfer function between the source and measuring points. The requirement for many overdetermined hologram points for extended sources to obtain high accuracy poses a problem for the practical applications of NAH. …”
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SELECTION FEATURES OF THE FIELDS REPRESENTATION FOR THE ADDITIONAL SOURCES OF THE PARTIAL DOMAINS by A.P. ZVEREV

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A Decision Support System for Optimal Building Cold Source Selection by Qing Li, Yan Dong, Rong-Guang Cao, Le Li, Zhi-Bin Chen

“…Based on existing approaches to construction project evaluation and the selection of cold source systems, we establish an evaluation index for building cold source system functional models and network structures. …”
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ABDviaMSIFAT: Abnormal Crowd Behavior Detection Utilizing a Multi-Source Information Fusion Technique by Ali Ahmad Hamid, S. Amirhassan Monadjemi, Bijan Shoushtarian

“…To solve this issue, we suggest a new method that combines data from various sources with different characteristics to enhance the precision of detecting human behavior in crowds. …”
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Flickers, Bursts, and Dips: Detecting Rapid Variability with the g(2) Autocorrelation Function by Brian C. Lacki

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Feature dependence graph based source code loophole detection method by Hongyu YANG, Haiyun YANG, Liang ZHANG, Xiang CHENG

“…Given the problem that the existing source code loophole detection methods did not explicitly maintain the semantic information related to the loophole in the source code, which led to the difficulty of feature extraction of loo-phole statements and the high false positive rate of loophole detection, a source code loophole detection method based on feature dependency graph was proposed.First, extracted the candidate loophole statements in the function slice, and gen-erated the feature dependency graph by analyzing the control dependency chain and data dependency chain of the candi-date loophole statements.Secondly, the word vector model was used to generate the initial node representation vector of the feature dependency graph.Finally, a loophole detection neural network oriented to feature dependence graph was constructed, in which the graph learning network learned the heterogeneous neighbor node information of the feature de-pendency graph and the detection network extracted global features and performed loophole detection.The experimental results show that the recall rate and F1 score of the proposed method are improved by 1.50%～22.32% and 1.86%～16.69% respectively, which is superior to the existing method.…”
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Feature dependence graph based source code loophole detection method by Hongyu YANG, Haiyun YANG, Liang ZHANG, Xiang CHENG

“…Given the problem that the existing source code loophole detection methods did not explicitly maintain the semantic information related to the loophole in the source code, which led to the difficulty of feature extraction of loo-phole statements and the high false positive rate of loophole detection, a source code loophole detection method based on feature dependency graph was proposed.First, extracted the candidate loophole statements in the function slice, and gen-erated the feature dependency graph by analyzing the control dependency chain and data dependency chain of the candi-date loophole statements.Secondly, the word vector model was used to generate the initial node representation vector of the feature dependency graph.Finally, a loophole detection neural network oriented to feature dependence graph was constructed, in which the graph learning network learned the heterogeneous neighbor node information of the feature de-pendency graph and the detection network extracted global features and performed loophole detection.The experimental results show that the recall rate and F1 score of the proposed method are improved by 1.50%～22.32% and 1.86%～16.69% respectively, which is superior to the existing method.…”
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Selenium Accumulating Leafy Vegetables Are a Potential Source of Functional Foods by Petro E. Mabeyo, Mkabwa L. K. Manoko, Amra Gruhonjic, Paul A. Fitzpatrick, Göran Landberg, Máté Erdélyi, Stephen S. Nyandoro

“…., Cucurbita maxima, Ipomoea batatas, Solanum villosum, Solanum scabrum, and Vigna unguiculata were explored for their capabilities to accumulate selenium when grown on selenium enriched soil and for use as a potential source of selenium enriched functional foods. Their selenium contents were determined by spectrophotometry using the complex of 3,3′-diaminobenzidine hydrochloride (DABH) as a chromogen. …”
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Derivation of pressure distribution models for horizontal well using source function by J.O. Oloro, E.S. Adewole

“…In this work, ten (10) models for pressure distribution for horizontal well under different boundary variation were derived following these steps for each of the models:(i) choosing a boundary condition for each axis (ii) selecting the appropriate source function for each axis and (iii)applying Newman product rule to arrive at the pressure expression. …”
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Go Source Code Vulnerability Detection Method Based on Graph Neural Network by Lisha Yuan, Yong Fang, Qiang Zhang, Zhonglin Liu, Yijia Xu

“…With the widespread application of the Go language, the demand for vulnerability detection in Go programs is increasing. Existing detection models and methods have deficiencies in extracting source code features of Go programs and mainly focus on detecting concurrency vulnerabilities. …”
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Source Type Recognition Algorithm of Radio Emission using Radial Basis Functions by V. F. Korotkov, R. S. Zyryanov

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Innovation as a Source of Ensuring the Sustainable Development of the Enterprise by Brahina Olha S.

“…The author determines the factors of importance of innovative development in the context of sustainable development and the main functions it performs. The stages of innovative development in the context of sustainable development of enterprise are formed, among which it is worth highlighting the following: analysis and assessment of the current state of enterprise; generation and selection of ideas; development of an innovation strategy; implementation of innovative solutions, monitoring and evaluation of efficiency, adjustment and improvement of strategy, communication of results and involvement of stakeholders. …”
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Resource selection by a megaomnivore in a marine foraging habitat by Andrew S. Maurer, Tomo Eguchi, Garrett E. Lemons, Robin A. LeRoux, Erin L. LaCasella, Calandra N. Turner Tomaszewicz, Megan E. Hanna, Jessica Curran, Bryant Chesney, Sheila V. Madrak, Jeffrey A. Seminoff

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Anomaly Usage Behavior Detection Based on Multi-Source Water and Electricity Consumption Information by Wenqing Zhou, Chaoqiang Chen, Qin Yan, Bin Li, Kang Liu, Yingjun Zheng, Hongming Yang, Hui Xiao, Sheng Su

“…Current resident anomaly detection technologies rely on single-source energy data, lacking detailed behavior pattern analysis. …”
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A fluorescence-enhanced inverse opal sensing film for multi-sources detection of formaldehyde by Xiaokang Lu, Bo Han, Deyilei Wei, Mingzhu Chu, Haojie Ma, Ran Li, Xueyan Hou, Yuqi Zhang, Jijiang Wang

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Unlocking the prebiotic carbohydrates: Insights into the types, preparation, health benefits and future utilizations of selected Indonesian exotic fruit seeds as a potential source... by Andri Cahyo Kumoro, Dyah Hesti Wardhani, Tutuk Djoko Kusworo, Yusuf Ma'rifat Fajar Azis, Misbahudin Alhanif, Tan Chin Ping

“…This paper aims to explore the feasibility of utilizing Indonesian exotic fruit seeds as a potential source of prebiotic carbohydrates for functional foods and dietary additives. …”
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Enhanced Reward Function Design for Source Term Estimation Based on Deep Reinforcement Learning by Junhee Lee, Hongro Jang, Minkyu Park, Hyondong Oh

“…This study investigates the design of reward functions for deep reinforcement learning-based source term estimation (STE). …”
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Sources of uncertainty in DNA metabarcoding of whole communities: Implications for its use in biomonitoring by J. Iwan Jones, Amanda Arnold, Dominik Buchner, John F. Murphy, Jasmine N. Childress, Pedro Beja, Florian Altermatt, Kristian Meissner, Ian Adams, Meri Anderson, Yaisel J. Borrell, Andrew G. Briscoe, Kat Bruce, Cátia Chaves, Chris Conyers, Kristy Deiner, Vasco Elbrecht, Álvaro Fueyo, Tim Goodall, Javier Granero‐Castro, Cristina Hartmann‐Fatu, Eleanor Jones, Elvira Mächler, Filipa M. S. Martins, Sam McGreig, Lyndall Pereira‐da‐Conceicoa, James L. Pretty, Daniel S. Read, Ben Price, Florian Leese, Rosetta C. Blackman

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Consistent Modeling of Transport Processes and Travel Times—Coupling Soil Hydrologic Processes With StorAge Selection Functions by Robin Schwemmle, Markus Weiler

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