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Density functional theory investigation of electronic structure and muon hyperfine interaction in isolated adenine and thymine

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Interactions 2024年第1期245卷 1-14页

作者： Zaharim, Wan Nurfadhilah Sulaiman, Shukri Jamaludin, Ammaina Rozak, Harison Watanabe, Isao Department of Chemical Sciences Faculty of Science and Technology Universiti Kebangsaan Malaysia Selangor Bangi43600 Malaysia Nuclear Structure Research Group RIKEN Nishina Center for Accelerator Based Science 2-1 Hirosawa Wako Saitama351-0198 Japan Physics Section School of Distance Education Universiti Sains Malaysia Pulau Pinang Minden11800 Malaysia Computational Physics Laboratory School of Distance Education Universiti Sains Malaysia Pulau Pinang Minden11800 Malaysia Innovative Global Program College of Engineering Shibaura Institute of Technology Saitama337-8570 Japan

In DFT calculation, a basis set is an important factor in determining the quality of results, especially for hyperfine interaction where changes in the local geometry and electronic structure can have a big impact. The use of extensive basis sets is always desirable and is expected to produce better results. However, for DNA molecules of more than 10 base-pairs, all-electron geometry optimization calculation using large basis sets such as 6-311++G(d,p) is prohibitively difficult even on a supercomputer. An alternative is to use smaller basis sets such as 6-31G which is used in this work. Thus, the objective of the work reported here is to determine whether 6-31G basis set is suitable to be used to study muon hyperfine interaction in short DNA molecule. It is found that in comparison with 6-311++G(d,p), 6-31G does not change the ranking of muon sites stability in isolated adenine and thymine. Furthermore, the 6-31G calculated muon HFCCs are slightly closer to the experimental values. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Electronic structure

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Prediction of Climatic and Water Quality in Urban Cooum River of Chennai Using Machine Learning Approaches 4th

Prediction of Climatic and Water Quality in Urban Cooum Rive...

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4th Doctoral Symposium on Computational Intelligence, DoSCI 2023

作者： Obaid, Ahmed J. Rajest, S. Suman Priscila, S. Silvia Shynu, T. Alkhafaji, Mohammed Ayad Mohsen, Karrar Shareef Faculty of Computer Science and Mathematics University of Kufa Kufa Iraq Bharath Institute of Higher Education and Research Tamil Nadu Chennai India Department of Biomedical Engineering Agni College of Technology Tamil Nadu Chennai India College of Engineering Technology National University of Science and Technology Dhi Qar Iraq Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Thi-Qar Iraq

ISBN: (纸本)9789819937158

Due to a large amount of plastic production, the river and river water are polluted. Most plastics wastages are not degradable. The river is polluted by macroplastics and microplastics. The manufacturing industries put their chemical waste into the river. The river water animals consume non-degradable plastics and industrial waste. It is very dangerous for river water animals to survive in such conditions. For the past 50 years, river water temperature and pH values have been calculated by using various machines like drifting buoys, ships, and the deepness of the river. This paper proposed a new machine learning approach that is used to calculate river surface temperature and pH level of the water and identify the types of litter in the river water. For our research, we have used a dataset obtained from the Cooum River, which has been running in the heart city of Chennai, located in India. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Rivers

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The Second Workshop on Child-Centered AI Design (CCAI)

The Second Workshop on Child-Centered AI Design (CCAI)

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2024 CHI Conference on Human Factors in Computing Sytems, CHI EA 2024

作者： Atabey, Ayça Wang, Ge Johnston, Samantha-Kaye Lin, Grace C. Wilson, Cara Urquhart, Lachlan D Zhao, Jun School of Law Centre for Data Culture & Society University of Edinburgh United Kingdom Department of Computer Science University of Oxford United Kingdom Scheller Teacher Education Program - the Education Arcade The Massachusetts Institute of Technology United States Centre for Research in Digital Education University of Edinburgh United Kingdom Edinburgh Law School University of Edinburgh United Kingdom

ISBN: (纸本)9798400703317

AI-powered solutions are increasingly woven into the fabric of children's digital worlds. They're found in interactive toys, home automation systems, everyday apps, and various online services that young users engage with. As we look ahead, it's almost certain that the prevalence of AI in tools and platforms designed for kids will grow, given AI's ability to offer rich, tailored, and dynamic experiences. However, the nuances of how these AI-centric platforms cater to children and how they can be optimized to meet the unique needs of younger users remain largely underexplored. Building on the momentum from our inaugural CCAI workshop at CHI 2023, our aspirations for this year's workshop include: (1) deepening the discourse on the essence of AI that prioritizes children, (2) focusing on actionable ethical strategies to operationalise child-centered AI design principles into practice, and (3) cultivating an ever-growing collective of professionals passionate about the future of child-focused AI innovations. © 2024 Owner/Author.

关键词： Automation

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Bioaccessibility of phenolic compounds from Brazilian grape juices using a digestion model with intestinal barrier passage

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Food Bioscience 2023年第2期52卷 1343-1351页

作者： Maria da Conceicao Prudencio Dutra Ana Beatriz Martins da Silva Ederlan de Souza Ferreira Ana Julia de Brito Araujo Carvalho Marcos dos Santos Lima Aline Camarao Telles Biasoto Graduate Program in Food Science Faculty of PharmacyFederal University of BahiaSalvadorBahiaBrazil Federal Institute of Education Science and Technology of Sertao PernambucanoLiquid Chromatography LaboratoryCampus IndustrialPetrolinaPernambucoBrazil Brazilian Agricultural Research Corporation Embrapa Semi-arid RegionPetrolinaPernambucoBrazil Brazilian Agricultural Research Corporation Embrapa EnvironmentJaguariunaSao PauloBrazil

Grape juices are rich in bioactive compounds;however,for these compounds to exert their functionality,they must be ***,the present study evaluated a simulated digestion process on the main bioactive compounds of monovarietal grape juices of five Brazilian hybrid cultivars(*** x ***).Characterization of the chemical profiles in liquid chromatography(HPLC-DAD-RID),behaviour of phenolics in the stages of digestion and bioaccessibility through the INFOGEST protocol plus intestinal barrier passage were carried *** the 24 polyphenols identified in the grape juice samples,11 were bioaccessible,with emphasis on the class of *** B2(101-426%),(+)-catechin(169-370%)and gallic acid(61-230%)stood out in all juices,showing that these compounds are key to the functionality of these *** were observed to differ between juices,demonstrating that factors such as the cultivar should be explored more extensively in studies on functional *** study also suggests that quality components such as sugars and organic acids influence the bioaccessibility of beverages.

关键词： Grape cultivars Functional beverages Phytochemicals Digestion simulation Bioactivity

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Cluster-Factors of Mobile Sensor Network technology for Security Enhanced PEGASIS

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第18期17卷 143-155页

作者： Sivakumar, T.K. Dhyani, Saurabh Manikanthan, S.V. Kaliappan, Vishnu Kumar Baskaran, K. Kishore, Kakarla Hari Department of Computing Technologies SRM Institute of Science and Technology Tamil Nadu Kattankulathur Chennai India Department of USCS Uttaranchal University Uttarakhand Dehradun India Melange Academic Research Associates Puducherry India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Tamil Nadu Coimbatore India Department of Biomedical Engineering Chennai Institute of Technology Tamil Nadu Chennai India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh India

Mobile wireless sensor networks (MWSNs) have been a hot topic of research, and numerous routing methods have been developed to increase energy efficiency and extend longevity. Nodes close to the sink often use more energy to transmit data from their neighbors to the sink, which causes them to run out of energy faster. These places are also referred to as rendezvous points, and choosing the best one is a hard task. The likelihood of choosing an ideal node as the rendezvous point will be extremely low because hierarchical algorithms only use their local information to select these places. The warm spot problem is addressed from four angles in this work using the Enhanced Power-Efficient Gathering in Sensor Information Systems (EPEGASIS) technique. In order to limit the amount of energy used during transmission, the ideal communication distance is first calculated. To balance the energy consumption among the nodes, mobile sink technology is employed once a threshold value is set to safeguard the dying nodes. The node can then modify its communication range based on how far away the sink node is from it. Thorough testing has been done to demonstrate that our suggested EPEGASIS works better in terms of longevity, drive usage, and web latency. © 2023 by the authors of this article. Published under CC-BY.

关键词： Energy efficiency

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Internet of Things (IoT) based Watercraft for Environmental Surveillance and Maintenance 5

Internet of Things (IoT) based Watercraft for Environmental ...

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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Sushmitha, S. Jebaraj, Bharath Singh Vivekrabinson, K. Azarudeen, K. Priyadharshini, S. Gnanasekaran, T. Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu India Kalasalingam Academy of Research and Educatuion Department of Computer Science and Engineeing Sriviliputhur India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India R.M.K. Engineering College Department of Electronics and Communication Engineering Thiruvallur India R.M.K. Engineering College Department of Electronics and Instrumentation Engineering Thiruvallur India

ISBN: (纸本)9798350367171

Water is the topmost priority for all human and industry needs in the universe. This proposed work creates awareness about the conservation of water bodies. Here, a garbage collector is designed to clean the lake and river automatically. The client can supervise the boat from any remote place. The developed model has a live camera that uses image processing technology for detecting the garbage, and the distance between the boat and the garbage is calculated, and the cleaning of plastic and other spills will happen. The values of pH and temperature are continuously monitored and the results are displayed on the web page using IoT. The proposed prototype has been experimented with in a small swimming pool. The proposed watercraft model is made up of heavy-duty material. The movement of the model is controlled using wireless Bluetooth communication from a smartphone application. The exact values of pH and temperature are displayed on the smartphone via an application. The mobile application has switches and can be used to control the boat. © 2024 IEEE.

关键词： Boats

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Self-derived Knowledge Graph Contrastive Learning for Recommendation 24

Self-derived Knowledge Graph Contrastive Learning for Recomm...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Shi, Lei Yang, Jiapeng Lv, Pengtao Yuan, Lu Kou, Feifei Luo, Jia Xu, Mingying State Key Laboratory of Media Convergence and Communication Communication University of China Beijing China Guangxi Key Laboratory of Digital Infrastructure Guangxi Zhuang Autonomous Region Information Center Nanning China Ministry of Education Zhengzhou China School of Data Science and Media Intelligence Communication University of China Beijing China School of Computer Science Beijing University of Posts and Telecommunications Beijing China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China Chongqing Research Institute Beijing University of Technology Chongqing China School of Information Science North China University of Technology Beijing China

ISBN: (纸本)9798400706868

Knowledge Graphs (KGs) serve as valuable auxiliary information to improve the accuracy of recommendation systems. Previous methods have leveraged the knowledge graph to enhance item representation and thus achieve excellent performance. However, these approaches heavily rely on high-quality knowledge graphs and learn enhanced representations with the assistance of carefully designed triplets. Furthermore, the emergence of knowledge graphs has led to models that ignore the inherent relationships between items and entities. To address these challenges, we propose a Self-Derived Knowledge Graph Contrastive Learning framework (CL-SDKG) to enhance recommendation systems. Specifically, we employ the variational graph reconstruction technique to estimate the Gaussian distribution of user-item nodes corresponding to the graph neural network aggregation layer. This process generates multiple KGs, referred to as self-derived KGs. The self-derived KG acquires more robust perceptual representations through the consistency of the estimated structure. Besides, the self-derived KG allows models to focus on user-item interactions and reduce the negative impact of miscellaneous dependencies introduced by conventional KGs. Finally, we apply contrastive learning to the self-derived KG to further improve the robustness of CL-SDKG through the traditional KG contrast-enhanced process. We conducted comprehensive experiments on three public datasets, and the results demonstrate that our CL-SDKG outperforms state-of-the-art baselines. © 2024 ACM.

关键词： Knowledge graph

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Molecular architecture of mammalian pyruvate dehydrogenase complex

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Protein & Cell 2025年第1期16卷 72-78页

作者： Maofei Chen Yutong Song Sensen Zhang Yitang Zhang Xudong Chen Minghui Zhang Meng Han Xin Gao Sai Li Maojun Yang Tsinghua-Peking Center for Life Sciences Beijing Frontier Research Center for Biological StructureSchool of Life SciencesTsinghua UniversityBeijing 100084China Ministry of Education Key Laboratory of Protein Science Tsinghua UniversityBeijing 100084China State Key Laboratory of Membrane Biology Tsinghua UniversityBeijing 100084China Department of Biochemistry and Molecular Biology School of Basic Medical SciencesShenzhen University Health Science CenterShenzhen 518060China Protein Research Technology Center Protein Chemistry and Omics PlatformSchool of Life SciencesTsinghua UniversityBeijing 100084China Computer Science Program ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia Computational Bioscience Research Center King Abdullah University of Science and TechnologyThuwal 23955-6900Kingdom of Saudi Arabia Cryo-EM Facility Center Southern University of Science and TechnologyShenzhen 518055China Beijing Life Science Academy Beijing 102209China

Dear Editor,Pyruvate dehydrogenase complex(PDHc) is a large multienzyme assembly(Mr = 4–10 million Daltons) consisting of three essential components: pyruvate dehydrogenase(E1p), dihydrolipoyl transacetylase(E2p), and dihydrolipoyl dehydrogenase(E3). These three enzymes perform distinct functions sequentially to catalyze the oxidative decarboxylation of pyruvate with formation of nicotinamide adenine dinucleotide(NADH) and acetyl-coenzyme A(Patel and Roche, 1990).

关键词： sequentially oxidative Dalton

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Integration of the grey relational analysis with machine learning for sucrose anaerobic hydrogen production prediction

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International Journal of Hydrogen Energy 2024年 68卷 388-397页

作者： Wang, Xianze Wang, Yifan Tseng, Yu-Yao Gao, Yidi Li, Keqing Wang, Ming-Hung Wu, Jinghui Lay, Chyi-How Huo, Mingxin Engineering Research Center of Low-Carbon Treatment and Green Development of Polluted Water in Northeast China Ministry of Education School of Environment Northeast Normal University Changchun130117 China Science and Technology Innovation Center for Municipal Wastewater Treatment and Water Quality Protection Northeast Normal University Changchun130117 China Key Laboratory of Songliao Aquatic Environment Ministry of Education Jilin Jianzhu University Changchun130118 China Master's Program of Green Energy Science and Technology Feng Chia University Taichung40724 Taiwan Department of Computer Science and Information Engineering National Chung Cheng University Chiayi County Taiwan

Anaerobic fermentation for hydrogen production is influenced by various environmental factors that can limit microbial activity. However, machine learning (ML) shows significant potential in explaining the complexity of biological processes. This study focuses on the application of ML in predicting hydrogen production rates (HPR) during anaerobic fermentation of biomass energy. Grey relation analysis was employed to investigate the correlation between operational parameters and HPR. Five ML algorithms, namely decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), and K-nearest neighbor (KNN), were utilized in conjunction with operating conditions and digestate characteristics as features, with HPR as the target variable. The evaluation of the models was conducted using mean squared error (MSE) and R squared (R2). Notably, butyrate, oxidation-reduction potential (ORP), and volatile suspended solids (VSS) were identified as key factors influencing hydrogen production during sucrose anaerobic fermentation. Among the ML algorithms tested, XGBoost achieved the highest accuracy, with an R2 value of 0.91 and an MSE of 0.0052. © 2024 Hydrogen Energy Publications LLC

关键词： Machine learning

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Terahertz dual-beam leaky-wave antenna based on composite spoof surface plasmon waveguide

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Optoelectronics Letters 2023年第2期19卷 72-76页

作者： BAI Yukun LI Sen Tianjin Key Laboratory of Film Electronic and Communication Devices Engineering Research Center of Optoelectronic Devices and Communication TechnologyMinistry of EducationSchool of Integrated Circuit Science and EngineeringTianjin University of TechnologyTianjin 300384China

In this paper, we propose a single-port dual-beam leaky-wave antenna(LWA) in the terahertz(THz) band based on a composite spoof surface plasmon polariton(SSPP) waveguide. The antenna can generate three independent transmission channels by exciting two independent modes inherent to hole and groove structures, respectively. By periodic modulation of the hole and groove structures, we achieve dual-beam scanning through a broad radiation angle using only the -1st space harmonics of the two modes, hence avoiding the instability of the -2rd space harmonic. Within the operating frequency range of 0.62—0.85 THz, the gain ranges from 13.5 d Bi to 17 d Bi for the backward beam, and from 6 d Bi to 11.8 d Bi for the forward beam. The antenna can accomplish continuous backward beam through broadside to forward beam scanning with a total scanning range of 116° and an average efficiency of about 92%. The antenna exhibits a great potential in the design of multi-transceiver radar system in the THz band and multi-beam LWAs.

关键词： Terahertz dual-beam leaky-wave antenna plasmon waveguide

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