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Acyl-anchored metal-organic cages with interior cryptand-like recognition sites for selective removal of radioactive strontium(Ⅱ)

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science Bulletin 2025年第5期70卷 683-693页

作者： Wei Jin Qunyan Wu Yan Lou Zhiwei Huang Feng Liu Bowen Hu Jipan Yu Kongqiu Hu Liyong Yuan Weiqun Shi Lei Mei College of Nuclear Science and Technology Harbin Engineering UniversityHarbin 150001China Laboratory of Nuclear Energy Chemistry Institute of High Energy PhysicsChinese Academy of SciencesBeijing 100049China School of Nuclear Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China

Tailored design of organic linkers or metal nodes can introduce desirable functionalities into metal-organic cages(MOCs),significantly expanding their potential *** this study,we present a viable approach for engineering acyl-type metal nodes to create interior oxygen-rich sites within MOCs,enabling specific recognition of metal ions,including radioactive contaminants,while maintaining the structural integrity of the MOCs.A novel MOC featuring a uranyl-sealed calix[4]resorcinarene(C[4]R)-based multisite cavity,referred to as UOC,is synthesized as a *** UOC,peroxide-bridged dimeric uranyl units at both ends of the coordination cage provide abundant oxygen sites for coordination,forming a cryptand-like cavity that facilitates efficient recognition and encapsulation of Sr2+due to the size-matching ***,hydrophobic binding cavities at both ends of UOC promote the co-inclusion of two different guest *** by the strong binding affinity of UOC for Sr2+,it is employed as a solid adsorbent for capturing low concentrations of Sr2+in strontium-contaminated simulated groundwater.A removal efficiency of 99.7%for Sr2+at an initial concentration as low as 0.013 mmol L−1 is achieved,demonstrating its significant potential for the selective removal of trace amounts of radioactive 90Sr2+.This work demonstrates the feasibility of incorporating acyl-type metal nodes into MOCs for the recognition and encapsulation of metal ions,offering a novel strategy for developing new MOC-based functional materials.

关键词： Metal-organic cages Supramolecular recognition Node engineering Radioactive strontium Selective removal

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A robust deformed convolutional neural network(CNN)for image denoising

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CAAI Transactions on Intelligence technology 2023年第2期8卷 331-342页

作者： Qi Zhang Jingyu Xiao Chunwei Tian Jerry Chun‐Wei Lin Shichao Zhang School of Economics and Management Harbin Institute of Technology at WeihaiWeihaiChina School of Computer Science Central South UniversityChangshaChina School of Software Northwestern Polytechnical UniversityXi'anShaanxiChina Research&Development Institute Northwestern Polytechnical UniversityShenzhenChina Yangtze River Delta Research Institute Northwestern Polytechnical UniversityTaicangChina Department of Computer Science Electrical Engineering and Mathematical SciencesWestern Norway University of Applied SciencesBergenNorway

Due to strong learning ability,convolutional neural networks(CNNs)have been developed in image ***,convolutional operations may change original distributions of noise in corrupted images,which may increase training difficulty in image *** relations of surrounding pixels can effectively resolve this *** by that,we propose a robust deformed denoising CNN(RDDCNN)in this *** proposed RDDCNN contains three blocks:a deformable block(DB),an enhanced block(EB)and a residual block(RB).The DB can extract more representative noise features via a deformable learnable kernel and stacked convolutional architecture,according to relations of surrounding *** EB can facilitate contextual interaction through a dilated convolution and a novel combination of convolutional layers,batch normalisation(BN)and ReLU,which can enhance the learning ability of the proposed *** address long-term dependency problem,the RB is used to enhance the memory ability of shallow layer on deep layers and construct a clean ***,we implement a blind denoising *** results demonstrate that our denoising model outperforms popular denoising methods in terms of qualitative and quantitative *** can be obtained at https://***/hellloxiaotian/RDDCNN.

关键词： blind denoising CNN deformed block enhanced block

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Effectual Energy Optimization Stratagems for Wireless Sensor Network Collections Through Fuzzy-Based Inadequate Clustering

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SN computer science 2024年第8期5卷 1022页

作者： S, Kavitha Rao, Deepak Dasaratha Jain, Anupriya Sharma, Seema Pandit, Shraddha V. Pandey, Rajeev Christ University Karanataka Banglore India Indian Institute of Technology Patna India School of Computer Applications Manav Rachna International Institute of Research and Studies(MRIIRS) Haryana Faridabad India Department of Artificial Intelligence and Data Science PES Modern College of Engineering Pune Shivajinagar 411005 India Department of Computer Science & amp Engineering University Institute of Technology Rajiv Gandhi Proudyogiki Vishwavidyalaya Bhopal Madhya Pradesh India

Wireless Sensor Networks (WSNs) are crucial in the burgeoning Internet of Things (IoT) landscape, serving as a backbone technology that enables myriad applications across various industries. Originating as a simple methodology, WSNs have evolved significantly, propelled by rapid advancements in sensor technology and hardware capabilities. These networks play a pivotal role in collecting and transmitting data, which is essential for the infrastructure of most IoT systems. WSNs operate by deploying sensor nodes across diverse locations to gather environmental data. This scalability and adaptability of WSNs were demonstrated in studies where network coverage was expanded to include 100 and 200 nodes. Notably, the implementation of the innovative FLECH (Fuzzy Logic Energy-efficient Clustering Hierarchy) protocol significantly enhanced energy efficiency, reducing consumption by 12.69% in networks with 100 nodes and by 36.85% in those with 200 nodes, compared to the traditional LEACH (Low-Energy Adaptive Clustering Hierarchy) protocol. This work innovatively combines fuzzy logic and Particle Swarm Optimization (PSO) for efficient Cluster Head selection in Wireless Sensor Networks. The evaluation of these protocols involved numerous simulations and communication tests to ascertain the First Node Die (FND) point—indicative of when a network begins to lose efficacy due to energy depletion. Results indicated that the LEACH protocol reached the FND point faster than FLECH, suggesting that FLECH may offer better longevity and durability for IoT applications, aligning with the needs for sustainable and efficient operation in expanding technological ecosystems. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： FLECH IoT LEACH WSN

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A New Wide-Angle, Low-Resolution Infrared Array Sensor with Improved DL Algorithm for Activity Detection 2

A New Wide-Angle, Low-Resolution Infrared Array Sensor with ...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Poorani, S. Raja, M. Karthick Parkavi, B. Pande, Soumitra Subodh Subbaiyan, Arunmurugan Kiruthiga, S. Kongu Engineering College Computer Technology-UG Perundurai India Sri Eshwar College of Engineering Computer Science & Engineering Kinathukadavu Coimbatore India Presidency University School of Cse & Is Bengaluru India Ericsson Global India Private Limited Bangalore India Ece Periyar Maniammai Institute of Science and Technology Tamilnadu Thanjavur India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India

ISBN: (纸本)9798350325799

A deep learning based method for activity detection that makes use of low-resolution, wide-angle infrared (IR) array sensors. In addition to the primary difficulty of this work - how to further enhance the performance of IR array sensor-based systems for activity detection also tackle the following issues: When comparing to a regular IR array sensor, a wide-angle sensor is used to get a better overall picture. As a result, it is difficult to learn how to adapt to the varying temperature distributions caused by activities performed in various locations. Additionally, in contrast to other efforts, aim to carry out the activity detection with the minimum amount of information feasible. This study intends to detect activity in a time frame of less than 1 second, whereas previous efforts have used a time window equivalent to 10 seconds. However, by using deep learning method outperform past work's accuracy while keeping the technique lightweight enough to operate on smartphones with limited processing capacity. Because of the neural network's capability to acquire the features, our hybrid deep learning model is particularly well adapted to the categorization of distorted photos. First, a Convolutional Neural Network (CNN) is used to differentiate between ceiling data and wall data, and then the combined data is also classified. Second, the CNN's output is fed into a Long Short-Term Memory (LSTM) with a window size equal to 5 frames so that the sequence of actions may be categorized. To detect the activity, our model uses a window size equal to 5 frames (i.e., less than a second). Our hybrid deep learning model achieves 99% accuracy in the total identification of activities while outperforming the state-of-the-art methods that use a longer time frame. © 2023 IEEE.

关键词： Convolutional neural networks

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Detection and Evaluation of Cybersecurity Threats in MANET Based on AI 4

Detection and Evaluation of Cybersecurity Threats in MANET B...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Mohandas, R. Vaigandla, Karthik Kumar Sivapriya, N. Kirubasankar, K. Chennai Institute of Technology Department of Electronics & Communication Engineering Tamil Nadu Chennai India Balaji Institute of Technology and Science Department of Electronics & Communication Engineering Telangana Warangal India Cauvery College for Women Department of Computer Applications Tamil Nadu Trichy India

ISBN: (纸本)9798331529635

This research focuses on the use of AI to enhance the security of Mobile Ad-hoc Networks (MANETs), which aredecentralized and mobile wireless networks characterized by their dynamic nature. The work aims to assess the risks associated with cyber activities and identify various sorts of cyber threats, such as Distributed Denial of Service (DDoS) attacks, malware invasions, data leaks and unauthorized access attempts. This will be achieved by using Artificial Intelligence (AI)-powered algorithms and models. The objective is to enhance the accuracy in identifying and categorizing these threats, hence increasing the security and dependability of MANET networks. Anomaly detection is a supplementary protection mechanism that specifically targets the hardware and network traffic of MANETs. Implementing a monitoring strategy is necessary in order to detect any anomalous activity that might potentially indicate security vulnerabilities or attacks in the MANET setups. The primary objective is to identify and address the unique characteristics of timely detection, which may enhance the security capabilities of MANET against cyber attacks. Machine learning (ML) algorithms often demonstrate impressive performance in efficiently identifying and accurately classifying various cyber threats, including DDoS attacks, malware infiltrations, and attempted unauthorized access. This anomaly detection technology effectively identifies robot abnormalities and malicious actions in network data, while also proactively avoiding system vulnerabilities or threats. In addition, this research discovered very effective AI-based cybersecurity solutions for dynamic decentralized MANET systems. These systems were specifically designed for tasks such as street-view switching, route finding, self healing, and self configuration. © 2024 IEEE.

关键词： Cyber attacks

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Blockchain-Based System for Virtual Power Plants: Enhancing Efficiency and Security in Operations and Transactions 8th

Blockchain-Based System for Virtual Power Plants: Enhancing ...

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8th International Conference on Internet of Things and Connected Technologies, ICIoTCT 2023

作者： Ganesh Kumar, G. Kanakaprabha, S. Gopal, Gaddam Venu Shaik, Riaz Srija, N. Ashok, K. Department of Information Technology Hindusthan Institute of Technology TN Coimbatore India Department of Computer Science and Engineering Rathinam Technical Campus TN Coimbatore India B V Raju Institute of Technology Telangana Narsapur Medak India Department of Internet of Things KL Deemed to be University AP Vijayawada India Department: Information Technology M. Kumarasamy College of Engineering TN Karur India Vignana Bharathi Institute of Technology Hyderabad Ghatkesar India

ISBN: (纸本)9789819757855

The concept of Virtual Power Plant (VPP) has become an attractive solution to optimize the integration of renewable energy sources into the grid. However, the operation and transaction system of VPPs still face several challenges, such as the lack of transparency, security, and efficiency. In this learning, we propose a new operation and transaction system built on blockchain technology for VPPs. The proposed system contains of two layers, the blockchain layer and the application layer. The blockchain layer is responsible for ensuring the security and transparency of transactions, while the application layer provides an interface for VPP operators and users. We implemented the proposed system using Ethereum, one of the most popular blockchain platforms. We also conducted a case study to evaluate the routine of the proposed system in terms of transaction throughput, latency, and scalability. The outcomes show that the proposed system can handle a large quantity of transactions with low latency and high scalability. Our study demonstrates the possible of blockchain technology to enhance the operation and transaction system of VPPs, which can contribute to the integration of renewable energy bases into the grid. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Blockchain Cybersecurity Decentralized consensus Energy trading Energy transactions Operational efficiency Renewable energy Smart contracts Virtual power plants VPP

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Unleashing Hidden Business Insights: Harnessing Unstructured Big Data through Text Analysis, NLP, and Visualizations for Budgetary Decisions in Governmental Organizations 12

Unleashing Hidden Business Insights: Harnessing Unstructured...

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12th International Electrical Engineering Congress, iEECON 2024

作者： Kongthong, Chanwit Jitkajornwanich, Kulsawasd Intakosum, Sarun School Of Science King Mongkut's Institute Of Technology Ladkrabang Department Of Computer Science Bangkok10520 Thailand College Of Media And Communication Texas Tech University LubbockTX79409 United States

ISBN: (纸本)9798350383591

Processing Thai language texts can be a challenge due to the complexities of the language, particularly texts from social media and online platforms. This paper introduces an analysis and visualization framework specifically designed to tackle the intricacies associated with processing the Thai language data within the context of online textual content, by utilizing natural language processing (NLP) and visualization techniques. The objectives of this study were to develop an effective Thai text data analysis and visualization framework that allows us to effectively and automatically get a better understanding of the content embedded in Thai textual data. The methodology initiated with a review of existing analysis frameworks and visualization techniques with a specific focus on Thai. The data collection phase encompassed a diverse corpus of Thai text data gathered from online sources. The selected data underwent preprocessing to address language-specific challenges. The proposed Thai analysis and visualization framework consists of multiple stages. Each stage is tailored to accommodate the intricacies of the Thai language, facilitating improved information extraction and text comprehension. The proposed visualization techniques utilize interactive graphs, such as bar charts, line charts, pie charts and donut charts, to offer intuitive and insightful representations of the processed data. Results from our case study show the effectiveness of our Thai analysis framework and visualization techniques in capturing crucial information from online contents written in Thai from governmental organizations. © 2024 IEEE.

关键词： Data visualization

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Internet of Things (IoT)-Based Smart Blood Bank Services: A Review 15

Internet of Things (IoT)-Based Smart Blood Bank Services: A ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Babu, J. Chinna Khan, Mudassir Nandini, K. Reddysekhar, E. Kumar, G. Pavan Sathwika, B. Annamacharya Institute of Technology and Sciences Department of Electronics and Communication Engineering Andhra Pradesh Rajampet India King Khalid University College of Science & Arts Tanumah Department of Computer Science Abha Saudi Arabia

ISBN: (纸本)9798350370249

This review includes various methods or concepts that have been proposed by various authors for enhancing the offerings of blood banks. Smart blood bank services have been the subject of a great deal of research. Authors from several fields have provided their suggested models. An automated blood bank that uses SMS was created by Dr. G. F. Ali Ahmed, Pavitra H. V., utilizing an embedded technology. By employing mobile applications and IoT cloud services, Saravanan Subbiah created an integrated circuit. Vandana Khare IOT-based blood bank services were provided by T. Sravani. Based on the suggested system for blood bank services, Fizakhan. On blood bank services, S.S. Thorat made a proposal. © 2024 IEEE.

关键词： Blood bank IOT Smart systems

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Making Health Care Smarter: The Role of Machine Learning Algorithms 6

Making Health Care Smarter: The Role of Machine Learning Alg...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Bisht, Amita Dhiman, Diksha Yamsani, Nagendar Uttaranchal Institute of Technology Uttaranchal University Department of Computer Science and Engineering Dehradun India Shivalik College of Engineering Department of Computer Science and Engineering Dehradun India School of Computer Science and Aartificial Intelligence SR University Warangal India

ISBN: (纸本)9798350304480

Machine learning algorithms make a significant contribution to disease prediction. The main objective of this is to support researchers and practitioners in choosing the most suitable machine learning algorithm for the healthcare sector. In today's world machine learning (ML) algorithm has a essential role in many sectors like medical diagnosis, predictive analytics, security and in finance. Many people today are dealing with serious illnesses that must be detected in the starting stages of development in order to begin therapy on schedule. The diseases may become incurable once reaching this stage. By using machine learning algorithms, we can discover the patterns and predict the diseases. This study attempts to briefly describe several machine learning approaches and to discuss the function of these algorithms in the field of healthcare, including diabetes, cancer diagnosis, brain tumor detection, bioinformatics, and many more applications. © 2023 IEEE.

关键词： Cancer Diagnosis Disease Prediction Diseases Healthcare Machine Learning

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Deep Learning Driven - Pest Classification and Sustainable Agriculture 3

Deep Learning Driven - Pest Classification and Sustainable A...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kumar, M. Sirish Usha, R. Reddy, B. Rama Kantha Ramachandra, S. Prakash, P. Jaya Swetha, B. Mohan Babu University School of Computing Dept. of Data Science India Madanapalle Institute of Technology and Science Madanapalle India Sri Venkateswara College of Engineering Tirupati India Mother Theresa Institute of Engineering and Technology Palamaner India Sri Venkateswara College of Engineering Department of It Tirupati India Mohan Babu University Dept. of Master of Computer Applications India

ISBN: (纸本)9798331509675

This study introduces a system designed to identify pests in crops and classify them as either beneficial or harmful. The project begins by providing a comprehensive overview of various pest identification methods, analyzing their strengths and limitations. To address the problem, a categorization approach is proposed that incorporates convolutional neural networks (CNN) to build the pest identification and classification model. The model was trained using a dataset containing images of different pest species. Extensive testing was conducted on a large dataset, and the system's performance was validated against multiple traditional classification models. The proposed framework achieved a classification accuracy of 90%, outperforming conventional methods. This research highlights agriculture's vital role in the economy and underscores the potential of advanced technologies in supporting sustainable farming practices. © 2025 IEEE.

关键词： Agriculture Sustainability Classification Accuracy Classification Model Convolutional Neural Network (CNN) Pest Classification

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