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Method for controlling phytoplankton distribution in fresh open water

International Journal of Environmental Studies 2024年第5期81卷 2130-2147页

作者： Moldasheva, Raushan Ismailova, Aisulu Turmukhanova, Gulnur Mailybayeva, Aiman Yelezhanova, Shynar Zhanuzakova, Zulfiya Zhamangara, Aizhan Department of Information Systems Faculty of Computer Systems and Vocational Education S. Seifullin Kazakh Agrotechnical University Astana Kazakhstan Department of Software Engineering Faculty of Physics Mathematics and Information Technology Atyrau University named after Kh. Dosmukhamedov Atyrau Kazakhstan Department of Computer Science Faculty of Physics Mathematics and Information Technology Atyrau University named after Kh. Dosmukhamedov Atyrau Kazakhstan Laboratory of Palaeobotany Astana Botanical Garden branch of the Institute of Botany and Phytointroduction Astana Kazakhstan

This paper presents a processed phytoplankton dataset from Lake Zerendi and other Kokshetau Upland lakes, Kazakhstan. The study aimed to develop an automated method for controlling the distribution of phytoplankton in fresh open water using a gradient descent approach with the search for a starting point for research. The paper also presents the reconstructed biomass indicators for changes in water temperature, with model calibration based on results from 5 years of surface sampling. A special feature of the proposed computer data processing is the cubic spline interpolation, which allows a higher calculation accuracy. This method reduces the sample size required, providing a comprehensive picture of the condition of the infrastructure and allowing for a more efficient response to emerging ecosystem issues. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Phytoplankton

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Heuristic Algorithm for Solving Travelling Salesman Problem: Adding the Endpoints of the Longest Edges 5

Heuristic Algorithm for Solving Travelling Salesman Problem:...

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5th International Conference on Problems of Cybernetics and Informatics, PCI 2023

作者： Nuriyeva, Fidan Dokuz Eylul University Department of Computer Science Izmir Turkey Institute of Control Systems Laboratory of Recognition Identification and Methods of Optimal Solutions Baku Azerbaijan

ISBN: (纸本)9798350319064

This study proposes, a new heuristic algorithm based on insertion heuristic to solve the symmetric Travelling Salesman Problem. In the proposed algorithm, firstly the two longest edges are selected and the starting tour passing through their endpoints is constructed, then the endpoints of the next longest edges are added to this tour and the solution passing through all the vertices is found. This problem is known to be NP-Hard. The proposed method was successfully tested on different-sized problems. The experimental results show that the proposed algorithm outperforms many existing methods. © 2023 IEEE.

关键词： Traveling salesman problem

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Pushing AI to wireless network edge: an overview on integrated sensing, communication, and computation towards 6G

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science China(Information sciences) 2023年第3期66卷 7-25页

作者： Guangxu ZHU Zhonghao LYU Xiang JIAO Peixi LIU Mingzhe CHEN Jie XU Shuguang CUI Ping ZHANG Shenzhen Research Institute of Big Data Future Network of Intelligence Institute (FNii) The Chinese University of Hong Kong (Shenzhen) School of Science and Engineering (SSE) The Chinese University of Hong Kong (Shenzhen) State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Peng Cheng Laboratory State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications

Pushing artificial intelligence(AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things(AIoT) in the sixth-generation(6G) era. This gives rise to an emerging research area known as edge intelligence, which concerns the distillation of human-like intelligence from the vast amount of data scattered at the wireless network edge. Typically, realizing edge intelligence corresponds to the processes of sensing, communication,and computation, which are coupled ingredients for data generation, exchanging, and processing, ***, conventional wireless networks design the three mentioned ingredients separately in a task-agnostic manner, which leads to difficulties in accommodating the stringent demands of ultra-low latency, ultra-high reliability, and high capacity in emerging AI applications like auto-driving and metaverse. This thus prompts a new design paradigm of seamlessly integrated sensing, communication, and computation(ISCC) in a taskoriented manner, which comprehensively accounts for the use of the data in downstream AI tasks. In view of its growing interest, this study provides a timely overview of ISCC for edge intelligence by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art advancements, and shedding light on the road ahead.

关键词： sixth-generation (6G) edge intelligence artificial intelligence of things (AIoT) integrated sensing communication and computation (ISCC)

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Shared Memory-based Algorithms for MPI Irregular Collective Operations

Shared Memory-based Algorithms for MPI Irregular Collective ...

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2022 IEEE International Multi-Conference on Engineering, computer and Information sciences, SIBIRCON 2022

作者： Romanyuta, Alexey Kurnosov, Mikhail Computer Systems Department Siberian State University of Telecommunications and Information Science Novosibirsk Russia Computer Systems Department Rzhanov Institute of Semiconductor Physics The Siberian Branch of the RAS Siberian State University of Telecommunications and Information Science Computer Systems Laboratory Novosibirsk Russia

ISBN: (纸本)9781665464802

Algorithms for MPI-Scatterv, MPI-Gatherv and MPI-Allgatherv collective operations using the shared memory of SMP/NUMA machine are proposed. The algorithms use an approach based on copying message fragments through queues in a shared memory segment (Copy-In-Copy-Out). The implementation is based on Open MPI and on average reduces the execution time by 20-40% for the MPI-Scatterv, MPI-Gatherv, MPI-Allgatherv operations compared to the coll/tuned component of the Open MPI library. © 2022 IEEE.

关键词： Memory architecture

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LLM4CP:Adapting Large Language Models for Channel Prediction

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Journal of Communications and Information Networks 2024年第2期9卷 113-125页

作者： Boxun Liu Xuanyu Liu Shijian Gao Xiang Cheng Liuqing Yang State Key Laboratory of Advanced Optical Communication Systems and Networks School of ElectronicsPeking UniversityBeijing 100871China Internet of Things Thrust The Hong Kong University of Science and Technology(Guangzhou)Guangzhou 511400China Internet of Things Thrust and Intelligent Transportation Thrust The Hong Kong University of Science and Technology(Guangzhou)Guangzhou 511400China Department of Electronic and Computer Engineering and Department of Civil and Environmental Engineering The Hong Kong University of Science and TechnologyHong KongChina

Channel prediction is an effective approach for reducing the feedback or estimation overhead in massive multi-input multi-output (m-MIMO) systems. However, existing channel prediction methods lack precision due to model mismatch errors or network generalization issues. Large language models (LLMs) have demonstrated powerful modeling and generalization abilities, and have been successfully applied to cross-modal tasks, including the time series analysis. Leveraging the expressive power of LLMs, we propose a pre-trained LLM-empowered channel prediction(LLM4CP)method to predict the future downlink channel state information (CSI) sequence based on the historical uplink CSI sequence. We fine-tune the network while freezing most of the parameters of the pre-trained LLM for better cross-modality knowledge transfer. To bridge the gap between the channel data and the feature space of the LLM,preprocessor, embedding, and output modules are specifically tailored by taking into account unique channel characteristics. Simulations validate that the proposed method achieves state-of-the-art (SOTA) prediction performance on full-sample, few-shot, and generalization tests with low training and inference costs.

关键词： channel prediction massive multi-input multi-output(m-MIMO) large language models(LLMs) fine-tuning time-series

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Tumor microenvironment-responsive drug self-delivery systems to treat cancer and overcome MDR

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Rare Metals 2025年第1期44卷 1-33页

作者： Ling-Mei Li Yi-Pin Xie Yu-Rong Qin Hai-Ping Chu Hui Xie De-Jin Zang Teng Liu School of Pharmaceutical Sciences and Institute of Materia Medica Shandong First Medical University and Shandong Academy of Medical SciencesNational Key Laboratory of Advanced Drug Delivery and Release SystemsKey Laboratory for Biotechnology Drugs of National Health Commission(Shandong Academy of Medical Sciences)Key Lab for Rare and Uncommon Diseases of Shandong ProvinceJinan 250117China Department of Computer Science City University of Hong KongHong Kong 999077China

Tumor microenvironment-responsive drug self-delivery systems utilize tumor microenvironment-responsive chemical bonds to link anti-tumor drugs,exploiting the hydrophilic and hydrophobic properties of different drugs to form amphiphilic prodrug molecules with self-assembly *** stimulation by specific factors in the tumor microenvironment,these amphiphilic prodrug molecules can release drugs at precise sites within the *** strategies significantly increase the drug concentration at the tumor site while effectively reducing the damage of anti-cancer drugs to normal *** to the advanced delivery strategies such as synergistic administration and controlled drug release,tumor microenvironment-responsive drug self-delivery systems hold great potential for treating malignant tumors with multidrug resistance(MDR).At the same time,the stimulus-reactivity of metal complexes provides an important opportunity to design site-specific prodrugs that can maximize therapeutic efficacy while minimizing adverse side effects of metal *** innovative drug design complements the tumor microenvironment-responsive self-delivery system,providing more feasible therapeutic strategies and possibilities in the field of cancer therapy and drug *** work provides a comprehensive review of recent advancements in drug self-delivery systems,offering insights into their potential applications in cancer therapy and MDR reversal.

关键词： Drug self-delivery system Metal drug Tumor microenvironment response Drug carrier Tumor multidrug resistance

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Generative AI-Driven Incentive Mechanism for Semantic Communications in RSMA Networks

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IEEE Transactions on Cognitive Communications and Networking 2025年第3期11卷 1708-1722页

作者： Liu, Dongqing Liu, Yongwen Zhang, Lei Hafid, Abdelhakim Khoukhi, Lyes Li, Mingyuan Liu, Yutao Dong, Yadong The First Affiliated Hospital of Zhengzhou University National Engineering Laboratory for Internet Medical Systems and Applications Zhengzhou China Zhengzhou University of Light Industry Software Engineering College Zhengzhou China Henan University of Technology College of Electrical Engineering Zhengzhou China University of Montreal Department of Computer Science and Operational Research Canada GREYC Laboratory ENSICAEN Normandie University France

This paper proposes a framework integrating Rate Splitting Multiple Access (RSMA), semantic communications, and generative AI for optimizing next-generation wireless networks. We present a unified model that combines RSMA with semantic communications to enhance both spectral efficiency and semantic fidelity. Our system model focuses on a downlink semantic communication system with a multi-antenna base station serving multiple single-antenna users. A dynamic contract-based incentive mechanism is developed to address user heterogeneity and information asymmetry in semantic RSMA scenarios. We introduce a diffusion model-based approach for joint optimization of resource allocation and contract design in RSMA systems. The semantic encoding process extracts key information, i.e., free-space detection, interest points, object attributes, and spatial relationships, from image data. A loss function is designed to train the semantic encoder and RSMA scheme, incorporating semantic extraction, partitioning, reconstruction, and task specific components. Our framework includes a multi-objective performance evaluation that considers both conventional metrics and semantic accuracy in a multi-user RSMA environment. We also define a multi-component semantic accuracy metric to assess the quality and utility of the extracted semantic information. Extensive simulation results demonstrate the superiority of our proposed framework over existing approaches in terms of system throughput, energy efficiency, and semantic fidelity across various network scenarios and user distributions. © 2015 IEEE.

关键词： Resource allocation

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Guest Editorial: Special Issue on Human-Machine Fusion Decision-Making for Emergency Handling

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IEEE Transactions on Automation science and Engineering 2025年 22卷 4427-4433页

作者： Wu, Edmond Q. Li, Jianqiang Chen, Guimin Yuce, Mehmet R. Islam, Shafiqul Ser, Javier Del Yu, Hui Liu, Peter Xiaoping Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China College of Computer and Software Engineering Shenzhen University Shenzhen518060 China State Key Laboratory for Manufacturing Xi'an Jiaotong University Xi'an710049 China Department of Electrical and Computer Systems Engineering Monash University MelbourneVIC3800 Australia Xavier University of Louisiana New OrleansLA70125 United States Tecnalia Institute Bizkaia48160 Spain School of Creative Technologies University of Portsmouth PortsmouthPO1 2UP United Kingdom Department of Systems and Computer Engineering Carleton University OttawaONKIS 5B6 Canada

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Adaptive Methods for Kernel Initialization of Convolutional Neural Network Model Applied to Plant Disease Classification 11

Adaptive Methods for Kernel Initialization of Convolutional ...

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11th IEEE Open Conference of Electrical, Electronic and Information sciences, eStream 2024

作者： Lagnaoui, Saloua Boumais, Khaoula Fallah, Saad El En-Naimani, Zakariae Haddouch, Khalid Matuzevicius, Dalius ENSA Sidi Mohamed Ben Abdellah University Laboratory Engineering Systems and Applications Fez Morocco Sidi Mohammed Ben Abdallah University ENSA Laboratory AI Data Science and Emerging Systems Fez Morocco Private University of Fez Systems and Sustainable Environment Laboratory Faculty of Engineering Sciences Fez Morocco ENSET Laboratory Computer Science AI and Cyber Security University of Hassan II Casablanca Mohammedia Morocco Vilnius Gediminas Technical University Faculty of Electronics Department of Electronic Systems Vilnius Lithuania

ISBN: (纸本)9798350352412

Convolutional Neural Networks are instrumental in artificial intelligence, especially in image processing, where their ability to autonomously learn hierarchical features has led to significant breakthroughs. However, the success of these models is intricately tied to the judicious choice of hyperparameters, which include the configuration of convolutional layers, activation functions, and kernel initialization methods. This research explores kernel initialization methods in Convolutional Neural Network models, seven diverse initialization methods (Glorot Uniform, Ones initialization, Zero initialization, Constant initialization, Random initialization, HeNormal initialization, and Orthogonal initialization) are comprehensively compared. The primary objective is to showcase the sensitivity of Convolutional Neural Networks to these various initialization techniques. The study not only aims to reveal the nuanced impact of kernel initialization but also introduces an adaptive method to enhance model performance. By delving into the intricacies of initialization methods, this research contributes to the improvement of Convolutional Neural Networks effectiveness, especially in critical applications like Plant Disease classification. © 2024 IEEE.

关键词： Convolutional neural networks

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Utilizing Web Scraping and Machine Learning for Improving Accuracy of Smartphone Price Prediction

Utilizing Web Scraping and Machine Learning for Improving Ac...

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2024 International Conference on Advances in Data Engineering and Intelligent Computing systems, ADICS 2024

作者： Vasanth Kumar, V. Tamizhselvan, C. Manoharan, L. Vishnu Sekhar, R. Vinoth Kumar, V. Hindustan Institute Of Technology And Science Department Of Electronics And Communication Engineering Chennai India School Of Computing Srm Institute Of Science And Technology Department Of Data Science And Business Systems Kattankulathur India Hindustan Institute Of Technology And Science Department Of Computer Science And Engineering Chennai India School Of Bioengineering Srm Institute Of Science And Technology Integrated Bioprocessing Laboratory Department Of Biotechnology Kattankulathur India

ISBN: (纸本)9798350364828

Mobile prices play a pivotal role in determining their popularity amongst consumers and their competitive standing within the market. As customers consider their budget while evaluating a mobile phone's specifications and design, estimating the price beforehand becomes essential to cater to their needs and expectations. Hence, accurately forecasting mobile phone prices is a vital step in the product launch process to remain competitive and assess the market dynamics and competitors. The primary aim of this research is to determine the true market value of mobile phones. In this research, the dataset was extracted by means of utilizing a web scraping process from the flipkart website. The main objective of this research is to fetch the most recent dataset based on the most recent features. The extracted dataset is fitted into regression models such as linear regression, decision tree regressor, random forest regressor, KNN regressor, and gradient boosting regressor and performs the comparative analysis. The models are evaluated based on the mean absolute error metric. © 2024 IEEE.

关键词： Decision trees

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