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EXPERIMENTAL FACILITY FOR NOBLE GAS TRANSPORT IN LIQUID LEAD

UPB Scientific Bulletin, Series B: Chemistry and Materials science 2025年第1期87卷 121-134页

作者： Ivan, Ana Hororoi, Marian Arva, Mihai Mincu, Marin Gugiu, Daniela Corbescu, Bogdan Vîlcu, Andrei Institute for Nuclear Research – ALFRED Project Implementation Pitesti Romania Doctoral School Materials Science and Engineering National University of Science and Technology POLITEHNICA Bucharest Romania Institute for Nuclear Research – Electronics Pitesti Romania Institute for Nuclear Research – Post Irradiation Laboratory Pitesti Romania

A proposed method for fuel failure detection in lead-cooled fast reactors involves the tagging of the fuel elements with isotopic mixtures of noble gases like xenon and krypton during manufacturing. By inserting inert gases with distinct isotopic composition into fuel pins, operators can trace and identify failed pins through mass spectrometry of the cover gas plenum, enhancing the reactor safety and reliability. However, knowledge gaps remain regarding fission gases transport in lead-cooled fast reactors. To address this, an experimental facility was designed to investigate noble gases migration in molten lead, focusing on the time delay between gas release and its detection in the cover gas. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Fast reactors

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Multi-Objective Optimization for NOMA-Based Mobile Edge Computing Offloading by Maximizing System Utility

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China Communications 2023年第12期20卷 156-165页

作者： Hong Qin Haitao Du Huahua Wang Li Su Yunfeng Peng Chongqing University of Post and Telecom Chongqing 400065China China Mobile Research Institute Beijing 100000China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China

Mobile Edge Computing(MEC)is a technology for the fifth-generation(5G)wireless communications to enable User Equipment(UE)to offload tasks to servers deployed at the edge of ***,taking both delay and energy consumption into consideration in the 5G MEC system is usually complex and ***-orthogonal multiple access(NOMA)enable more UEs to offload their computing tasks to MEC servers using the same spectrum resources to enhance the spectrum efficiency for 5G,which makes the problem even more complex in the NOMA-MEC *** this work,a system utility maximization model is present to NOMA-MEC system,and two optimization algorithms based on Newton method and greedy algorithm respectively are proposed to jointly optimize the computing resource allocation,SIC order,transmission time slot allocation,which can easily achieve a better trade-off between the delay and energy *** simulation results prove that the proposed method is effective for NOMA-MEC systems.

关键词： computation offloading mobile edge computing non-orthogonal multiple access resource allocation

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On the State of NLP Approaches to Modeling Depression in Social Media: A post-COVID-19 Outlook

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IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4439-4451页

作者： Bucur, Ana-Maria Moldovan, Andreea-Codrina Parvatikar, Krutika Zampieri, Marcos Khudabukhsh, Ashiqur R. Dinu, Liviu P. University of Bucharest Interdisciplinary School of Doctoral Studies Bucharest050663 Romania Universitat Politècnica de València PRHLT Research Center València46020 Spain Rochester Institute of Technology RochesterNY14623 United States George Mason University Fairfax22030 United States University of Bucharest Human Language Technology Research Center Department of Computer Science Faculty of Mathematics and Computer Science Bucharest050663 Romania

Computational approaches to predicting mental health conditions in social media have been substantially explored in the past years. Multiple reviews have been published on this topic, providing the community with comprehensive accounts of the research in this area. Among all mental health conditions, depression is the most widely studied due to its worldwide prevalence. The COVID-19 global pandemic, starting in early 2020, has had a great impact on mental health worldwide. Harsh measures employed by governments to slow the spread of the virus (e.g., lockdowns) and the subsequent economic downturn experienced in many countries have significantly impacted people's lives and mental health. Studies have shown a substantial increase of above 50% in the rate of depression in the population. In this context, we present a review on natural language processing (NLP) approaches to modeling depression in social media, providing the reader with a post-COVID-19 outlook. This review contributes to the understanding of the impacts of the pandemic on modeling depression in social media. We outline how state-of-the-art approaches and new datasets have been used in the context of the COVID-19 pandemic. Finally, we also discuss ethical issues in collecting and processing mental health data, considering fairness, accountability, and ethics. © 2013 IEEE.

关键词： COVID-19

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Emerging and Changing Patterns in Prevalence of Anti-leptospiral Antibodies Against Different Serogroups in Livestock in Andaman-Islands Ecosystem

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Proceedings of the National Academy of sciences India Section B - Biological sciences 2024年第5期94卷 977-983页

作者： Kumar, K. Vinod Swathi, M. Bokade, Prajakta P. Bharath, V. SowjanyaKumari, S. Sunder, Jai Hemadri, D. Shome, B.R. Balamurugan, V. Indian Council of Agricultural Research -National Institute of Veterinary Epidemiology and Disease Informatics (ICAR-NIVEDI) Karnataka Yelahanka Bengaluru 560 064 India Animal Science Division ICAR- Central Island Agricultural Research Institute Andaman & Nicobar Islands Port Blair 744101 India National Post-Doctoral Fellowship Science and Engineering Research Board Department of Science and Technology Government of India New Delhi India

This study investigated the relative distribution of Leptospira serovars and changing patterns of anti-leptospiral antibodies in the endemic Andaman Islands of India. A total of 490 purposive random serum samples from cattle (n = 386) and goats (n = 104) were collected from December 2019 to January 2020 and were tested in a microscopic agglutination test (MAT) using a panel of 17 reference Leptospira serovars comprising 16 serogroups including two serovars of intermediate Leptospira species. The total overall seroprevalence of 17.6% (86/490) was observed, with 17.6% attributed to cattle and 17.3% to goats. The major reactive serogroup-specific anti-leptospiral antibodies observed were Icterohaemorrhagiae (32.6%), Hardjo (15.1%), Hebdomadis (11.6%), Grippotyphosa (10.5%), Pomona (9.3%), Tarassovi (9.3%), amongst others. This investigation provides evidence for the circulation and emergence of pathogenic Tarassovi and Djasiman serovars and intermediate Hurstbridge serovar in the livestock of South Andaman. This study’s findings would help further strengthen the one-health strategy and mitigate the leptospirosis burden in the Andaman Islands ecosystem of India. © The Author(s), under exclusive licence to The National Academy of sciences, India 2024.

关键词： Andaman Islands India Leptospirosis Livestock MAT Predominant Leptospira serovars

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Chemical profiling and potential use of Salvia officinalis L. essential oil as biological alternative for weed control

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Vegetos 2024年 1-8页

作者： Souihi, Mouna Khammassi, Marwa Amri, Ismail Kouki, Habiba Hanana, Mohsen Hamrouni, Lamia Mabrouk, Yassine The National Center for Nuclear Sciences and Technologies Laboratory of Biotechnology and Nuclear Technology Ariana Tunisia Doctoral School of Computer Science Communications Design and Environment (STICODE) Manouba Tunisia National Research Institute of Rural Engineering Water and Forests Ariana Tunisia

Natural plant resources can serve as alternatives to synthetic chemical products. Consequently, various plant species from different genera have recently been identified for their biological activities, such as Salvia officinalis L. The analysis of essential oils and fatty acid compounds was conducted using GC–MS. Essential oil extracted from the leaves and flowers, is highly rich in oxygenated monoterpenes. Eucalyptol (26.4–17.5%), α-thujone (11.9–9.56%), and β-thujone (22.9–10.33%) were the primary compounds in the leaves and flowers, respectively. The leaves also contained six fatty acid compounds, distinguished by a notable proportion of polyunsaturated fatty acids (PUFA) at 58.59%. Among the fatty acids profiled, linoleic acid was the most abundant, constituting 51.1% of the total compounds. The control of weed by S. officinalis essential oil was based on inhibition of germination and seedling growth of Sinapis arvensis L., Trifolium campestre Schreb., and Lepidium sativum at a concentration of 4 µL mL⁻¹. Additionally, the essential oil impacted the malondialdehyde content and electrolyte leakage of treated seedlings. © The Author(s) under exclusive licence to Society for Plant research 2024.

关键词： Essential oil GC–MS Phytotoxicity Salvia officinalis Weeds

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Screening of Autism Spectrum Disorder using Machine Learning Approach in Accordance with DSM-5 7

Screening of Autism Spectrum Disorder using Machine Learning...

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7th International Conference on Electronics, Materials Engineering and Nano-technology, IEMENTech 2023

作者： Bose, Shreea Seth, Prakriti St Xavier's College Department of Post Graduate and Research Computer Science Kolkata India National Institute of Technology Department of Biotechnology and Medical Engineering Rourkela India

ISBN: (纸本)9798350305517

Autism Spectrum Disorder (ASD) is a specific category of neurodevelopmental disorder that can be associated with several behavioral conditions and has no known cure to date. ASD can be detected from a very early stage in childhood and upon successful detection can be ameliorated. There have been several clinical diagnosis procedures and they can be error-prone and time-consuming. Thus, machine learning-based prediction models for early-stage ASD as well as in adolescents and adults are being developed over the years. In our study, several parameters of ASD detection were implemented with open-source ASD datasets and analysed using several machine learning models like Logistic regression, XGboost, SVC and Naive Bayes. Among these XGboost showed the best performance. The outcomes of such analytical approaches demonstrate that, when suitably optimized, machine-learning techniques can offer robust predictions of Autism Spectrum Disorder (ASD) status. These findings imply that it may be feasible to employ these models for the early ASD detection, thereby enhancing the prospects of timely and effective intervention. XGBoost has given best results throughout all datasets, including cross validation. An accuracy of 100% has been achieved, making the model best for prediction. © 2023 IEEE.

关键词： Diagnosis

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Information-Centric IoT-Based Smart Farming with Dynamic Data Optimization

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computers, Materials & Continua 2023年第2期74卷 3865-3880页

作者： Souvik Pal Hannah VijayKumar D.Akila N.Z.Jhanjhi Omar A.Darwish Fathi Amsaad Department of Computer Science and Engineering Sister Nivedita UniversityKolkataIndia Post Doctoral Researcher Sambalpur UniversitySambalpurIndia Department of Computer Science Anna Adarsh College for WomenChennaiIndia Department of Information Technology Vels Institute of ScienceTechnology and Advanced StudiesChennaiIndia School of Computer Science and Engineering SCETaylor’s UniversitySubang Jaya47500SelangorMalaysia FITTaylor’s UniversitySubang Jaya47500SelangorMalaysia Department of Information Security and Applied Computing Eastern Michigan UniversityUSA School of Information Security and Applied Computing Eastern Michigan UniversityUSA

Smart farming has become a strategic approach of sustainable agriculture management and monitoring with the infrastructure to exploit modern technologies,including big data,the cloud,and the Internet of Things(IoT).Many researchers try to integrate IoT-based smart farming on cloud platforms *** define various frameworks on smart farming and monitoring system and still lacks to define effective data management *** IoT-cloud systems involve massive structured and unstructured data,data optimization comes into the ***,this research designs an Information-Centric IoT-based Smart Farming with Dynamic Data Optimization(ICISF-DDO),which enhances the performance of the smart farming infrastructure with minimal energy consumption and improved ***,a conceptual framework of the proposed scheme and statistical design model has beenwell *** information storage and management with DDO has been expanded individually to show the effective use of membership parameters in data *** simulation outcomes state that the proposed ICISF-DDO can surpass existing smart farming systems with a data optimization ratio of 97.71%,reliability ratio of 98.63%,a coverage ratio of 99.67%,least sensor error rate of 8.96%,and efficient energy consumption ratio of 4.84%.

关键词： Smart farming data optimization information management internet of things cloud

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Behavioral Decision-Making of Key Stakeholders in Public-Private Partnerships:A Hybrid Method and Benefit Distribution Study

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computer Modeling in Engineering & sciences 2023年第9期136卷 2895-2934页

作者： Guoshuai Sun Wanyi Zhang Jiuying Dong Shuping Wan Jiao Feng School of Economics and Management Liaoning University of TechnologyJinzhou121001China School of Information Management Jiangxi University of Finance and EconomicsNanchang330032China Post-Doctoral Research Center Zhongda Construction Co.Ltd.Guangzhou510280China School of Science Shanghai Institute of TechnologyShanghai201418China School of Maritime Economics and Management Dalian Maritime UniversityDalian116026China

Public-private partnerships(PPPs)have been used by governments around the world to procure and construct infrastructural *** relies on private sector expertise and funding to achieve this lofty ***,given the uncertainties of project management,transparency,accountability,and expropriation,this phenomenon has gained tremendous attention in recent years due to the important role it plays in curbing infrastructural deficits ***,the reasonable benefit distribution scheme in a PPP project is related to the behavior decisionmaking of the government and social capital,aswell as the performance of the *** this paper,the government and social capital which are the key stakeholders of PPP projects were selected as the research *** on the fuzzy expected value model and game theory,a hybrid method was adopted in this research taking into account the different risk preferences of both public entities and private parties under the fuzzy demand *** alleviate the problem of insufficient utilization of social capital in a PPP project,this paper seeks to grasp the relationship that exists between the benefit distribution of stakeholders,their behavioral decision-making,and project performance,given that they impact the performance of both public entities and private parties,as well as assist in maximizing the overall utility of the ***,four game models were constructed in this study,while the expected value and opportunity-constrained programming model for optimal decision-making were derived using alternate perspectives of both centralized decision-making and decentralized ***,the optimal behavioral decision-making of public entities and private parties in four scenarios was discussed and thereafter compared,which led to an ensuing discussion on the benefit distribution system under centralized ***,based on an example case,the influence of different confiden

关键词： PPP fuzzy expected value game theory behavioral decision-making benefit distribution hybrid method

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Connected Equitable Cake Division via Sperner’s Lemma

arXiv

引用

arXiv 2024年

作者： Bhaskar, Umang Sricharan, A.R. Vaish, Rohit Tata Institute of Fundamental Research India Faculty of Computer Science Doctoral School of Computer Science University of Vienna Austria Indian Institute of Technology Delhi India

We study the problem of fair cake-cutting where each agent receives a connected piece of the cake. A division of the cake is deemed fair if it is equitable, which means that all agents derive the same value from their assigned piece. Prior work has established the existence of a connected equitable division for agents with nonnegative valuations using various techniques. We provide a simple proof of this result using Sperner’s lemma. Our proof extends known existence results for connected equitable divisions to significantly more general classes of valuations, including nonnegative valuations with externalities, as well as several interesting subclasses of general (possibly negative) valuations. © 2024, CC BY-NC-SA.

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Distributed Machine Learning System for Intrusion Detection 2

Distributed Machine Learning System for Intrusion Detection

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Patil, Harshal Lohumi, Yogesh Tejaswini, K.M. Sudheendramouli, H.C. Parikh, Swapnil Maranan, Ramya Sri Balaji University Pune India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Sri Siddhartha Institute of Technology Department of Mathematics Tumakuru India Gsss Simha SubbaMahalakshmi First Grade College Department of Computer Science and Application Mysuru India Parul University Department of Computer Science and Engineering Parul Institute of Engineering and Technology Post Limda India Saveetha School of Engineering Simats Department of Research and Innovation Chennai India

ISBN: (纸本)9798350373110

As network systems expand in complexity, the necessity for effective intrusion detection grows. This study compares two distributed machine learning systems utilizing different communication topologies: Parameter Server and Point-to-Point. The former employs centralized parameter management for efficient communication, while the latter facilitates direct node-to-node communication without a central server. Using extensive network traffic data, the models are assessed on performance metrics like accuracy, scalability, and false positive rates. Both systems show promising results, with median accuracy and F1 scores around 93%. However, limitations include potential data homogeneity issues, scalability challenges particularly with the Parameter Server becoming a bottleneck, concerns over model overfitting, and high computational overhead in maintaining models across numerous nodes. Addressing these drawbacks is crucial for improving the reliability of intrusion detection systems in complex network environments. © 2024 IEEE.

关键词： Network intrusion

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