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Comparative analysis of soil thermal conductivity in the permafrost regions of the Qinghai–Tibetan Plateau and the Arctic

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Geoderma 2025年 460卷

作者： Wenhao Liu Ren Li Tonghua Wu Guojie Hu Xiaodong Wu Shenning Wang Junjie Ma Jianzong Shi Shengfeng Tang Xiaofan Zhu Yongping Qiao Cryosphere Research Station on the Qinghai–Tibet Plateau State Key Laboratory of Cryospheric Science and Frozen Soil Engineering Northwest Institute of Eco-Environment and Resources Chinese Academy of Sciences Lanzhou Gansu 730000 China Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands Northwest Institute of Eco-Environment and Resources Chinese Academy of Sciences Lanzhou 730000 China University of Chinese Academy of Sciences Beijing 100049 China Key Laboratory of Knowledge Computing and Intelligent Decision Gansu Province Lanzhou 730000 China School of Geographic Science and Tourism Nanyang Normal University NanYang 473061 China

The Qinghai-Tibetan Plateau (QTP) and the Arctic are prime examples of permafrost distribution in high-altitude and high-latitude regions. A nuanced understanding of soil thermal conductivity (STC) and the various influencing factors is essential for improving the accuracy of permafrost simulation models in these areas. Nevertheless, no comparative analysis of STC between these two regions has been conducted. Therefore, this study aims to investigate the characteristics and influencing factors of STC at varying depths within the active layer (5 to 60 cm) during freezing and thawing periods in the QTP and the Arctic, using the regional-scale STC data products simulated through the XGBoost method. The findings indicate the following: (1) the mean STC of permafrost in the QTP is higher than that in the Arctic permafrost region. The STC in the QTP demonstrates a declining trend over time, while the Arctic permafrost maintains relative stability. The mean STC values in the QTP permafrost region during the thawing period are significantly higher than those during the freezing period. (2) STC of the QTP exhibits a fluctuating pattern at different depths, in contrast, the average STC value in the Arctic increases steadily with depth, with an increase rate of approximately 0.005 Wm −1 K −1 /cm. (3) The analysis of influencing factors revealed that although moisture content, bulk density, and porosity are the primary drivers of regional variations in STC between the QTP and the Arctic permafrost, moisture elements in the QTP region have a greater influence on STC and the effect is stronger with increasing depth and during the freeze–thaw cycles. Conversely, soil saturation, bulk density, and porosity in the Arctic have significant impacts. This study constitutes the first systematic comparative analysis of STC characteristics.

关键词： Soil thermal conductivity Soil moisture Permafrost QTP Arctic Climate change Clay% Clay component content Sand% Sand component content Silt% Silt component content Q tz Quartz component content S Thermal conductivity of the soil matrix R s Soil density R b Soil bulk density n Soil porosity W Soil volumetric water content S r Soil saturation STC Soil thermal conductivity R 2 Coefficient of Determination MAE Mean Absolute Error RMSE Root Mean Squared Error NSE Nash Efficiency Coefficient VAF Variance Accounting Factor ANN Artificial Neural Network PSO-ANN Particle Swarm Optimization-Artificial Neural Network GA-ANN Genetic Algorithm-Artificial Neural Network SVM Support Vector Machine PSO-SVM Particle Swarm Optimization-Support Vector Machine IPSO-SVM Improved Particle Swarm Optimization-Support Vector Machine RF Random Forest Bagging Bootstrap Aggregating XGBoost Extreme Gradient Boosting QTP Qinghai-Tibet plateau S cc Spearman correlation coefficient Adj. R 2 Adjusted Coefficient of Determination MSE Mean Squared Error sMAPE Symmetric Mean Absolute Percentage Error PI Performance Index WIA Willmott’s Index of Agreement

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A meta-inspired termite queen algorithm for global optimization and engineering design problems

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Engineering Applications of Artificial Intelligence 2022年 111卷

作者： Peng Chen Shihua Zhou Qiang Zhang Nikola Kasabov Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian 116622 China School of Computer Science and Technology Dalian University of Technology Dalian 116024 China Knowledge Engineering and Discovery Research Institute Auckland University of Technology Auckland 1010 New Zealand Intelligent Systems Research Center Ulster University Londonderry BT52 1SA UK

This paper proposes a novel bio-inspired termite queen algorithm (TQA) to solve optimization problems by simulating the division of labor in termite populations under a queen’s rule. TQA is benchmarked on a set of 23 functions to test its performance at solving global optimization problems, and applied to six real-world engineering design problems to verify its reliability and effectiveness. Comparative simulation studies with other algorithms are conducted, from whose results it is observed that TQA satisfactorily solves global optimization problems and engineering design problems.

关键词： Termite queen algorithm Benchmarked functions Loss minimization Metaheuristic technique Engineering design problems

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Unlocking the Secrets Behind Advanced Artificial Intelligence Language Models in Deidentifying Chinese-English Mixed Clinical Text: Development and Validation Study

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Journal of Medical Internet Research 2024年第1期26卷 e48443页

作者： Lee, You-Qian Chen, Ching-Tai Chen, Chien-Chang Lee, Chung-Hong Chen, Peitsz Wu, Chi-Shin Dai, Hong-Jie Dialogue System Technical Department Asustek Computer Inc Taipei Taiwan Intelligent System Laboratory Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Bioinformatics and Medical Engineering Asia University Taichung Taiwan Center for Precision Health Research Asia University Taichung Taiwan Electromagnetic Sensing Control and AI Computing System Laboratory Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Knowledge Discovery and Data Mining Lab Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Chemical Engineering Feng Chia University Taichung Taiwan National Center for Geriatrics and Welfare Research National Health Research Institutes Zhunan Taiwan National Institute of Cancer Research National Health Research Institutes Tainan Taiwan School of Post-Baccalaureate Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Center for Big Data Research Kaohsiung Medical University Kaohsiung Taiwan

Background: The widespread use of electronic health records in the clinical and biomedical fields makes the removal of protected health information (PHI) essential to maintain privacy. However, a significant portion of information is recorded in unstructured textual forms, posing a challenge for deidentification. In multilingual countries, medical records could be written in a mixture of more than one language, referred to as code mixing. Most current clinical natural language processing techniques are designed for monolingual text, and there is a need to address the deidentification of code-mixed text. Objective: The aim of this study was to investigate the effectiveness and underlying mechanism of fine-tuned pretrained language models (PLMs) in identifying PHI in the code-mixed context. Additionally, we aimed to evaluate the potential of prompting large language models (LLMs) for recognizing PHI in a zero-shot manner. Methods: We compiled the first clinical code-mixed deidentification data set consisting of text written in Chinese and English. We explored the effectiveness of fine-tuned PLMs for recognizing PHI in code-mixed content, with a focus on whether PLMs exploit naming regularity and mention coverage to achieve superior performance, by probing the developed models’ outputs to examine their decision-making process. Furthermore, we investigated the potential of prompt-based in-context learning of LLMs for recognizing PHI in code-mixed text. Results: The developed methods were evaluated on a code-mixed deidentification corpus of 1700 discharge summaries. We observed that different PHI types had preferences in their occurrences within the different types of language-mixed sentences, and PLMs could effectively recognize PHI by exploiting the learned name regularity. However, the models may exhibit suboptimal results when regularity is weak or mentions contain unknown words that the representations cannot generate well. We also found that the availability of cod

关键词： ChatGPT code mixing deidentification electronic health record large language model pretrained language model

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Evaluating associative classification algorithms for Big Data

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Big Data Analytics 2019年第1期4卷 1-27页

作者： Padillo, Francisco Luna, José María Ventura, Sebastián Department of Computer Science and Numerical Analysis University of Cordoba Cordoba Spain Knowledge Discovery and Intelligent Systems in Biomedicine Laboratory Maimonides Biomedical Research Institute of Cordoba Cordoba Spain Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

Associative Classification, a combination of two important and different fields (classification and association rule mining), aims at building accurate and interpretable classifiers by means of association rules. A major problem in this field is that existing proposals do not scale well when Big Data are considered. In this regard, the aim of this work is to propose adaptations of well-known associative classification algorithms (CBA and CPAR) by considering different Big Data platforms (Spark and Flink). An experimental study has been performed on 40 datasets (30 classical datasets and 10 Big Data datasets). Classical data have been used to find which algorithms perform better sequentially. Big Data dataset have been used to prove the scalability of Big Data proposals. Results have been analyzed by means of non-parametric tests. Results proved that CBA-Spark and CBA-Flink obtained interpretable classifiers but it was more time consuming than CPAR-Spark or CPAR-Flink. In this study, it was demonstrated that the proposals were able to run on Big Data (file sizes up to 200 GBytes). The analysis of different quality metrics revealed that no statistical difference can be found for these two approaches. Finally, three different metrics (speed-up, scale-up and size-up) have also been analyzed to demonstrate that the proposals scale really well on Big Data. The experimental study has revealed that sequential algorithms cannot be used on large quantities of data and approaches such as CBA-Spark, CBA-Flink, CPAR-Spark or CPAR-Flink are required. CBA has proved to be very useful when the main goal is to obtain highly interpretable results. However, when the runtime has to be minimized CPAR should be used. No statistical difference could be found between the two proposals in terms of quality of the results except for the interpretability of the final classifiers, CBA being statistically better than CPAR.

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iSplit LBI: Individualized partial ranking with ties via split LBI

arXiv

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arXiv 2019年

作者： Xu, Qianqian Sun, Xinwei Yang, Zhiyong Cao, Xiaochun Huang, Qingming Yao, Yuan Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS Microsoft Research Asia State Key Laboratory of Information Security Institute of Information Engineering CAS School of Cyber Security University of Chinese Academy of Sciences School of Computer Science and Tech. University of Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management CAS Peng Cheng Laboratory Department of Mathematics Hong Kong University of Science and Technology Hong Kong

Due to the inherent uncertainty of data, the problem of predicting partial ranking from pairwise comparison data with ties has attracted increasing interest in recent years. However, in real-world scenarios, different individuals often hold distinct preferences. It might be misleading to merely look at a global partial ranking while ignoring personal diversity. In this paper, instead of learning a global ranking which is agreed with the consensus, we pursue the tie-aware partial ranking from an individualized perspective. Particularly, we formulate a unified framework which not only can be used for individualized partial ranking prediction, but also be helpful for abnormal user selection. This is realized by a variable splitting-based algorithm called iSplitLBI. Specifically, our algorithm generates a sequence of estimations with a regularization path, where both the hyperparameters and model parameters are updated. At each step of the path, the parameters can be decomposed into three orthogonal parts, namely, abnormal signals, personalized signals and random noise. The abnormal signals can serve the purpose of abnormal user selection, while the abnormal signals and personalized signals together are mainly responsible for individual partial ranking prediction. Extensive experiments on simulated and real-world datasets demonstrate that our new approach significantly outperforms state-of-the-art alternatives. The code is now availiable at https://***/qianqianxu010/ NeurIPS2019-iSplitLBI. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

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Topic recommendation using Doc2Vec

Topic recommendation using Doc2Vec

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International Joint Conference on Neural Networks

作者： Petros Karvelis Dimitris Gavrilis George Georgoulas Chrysostomos Stylios Laboratory of Knowledge and Intelligent Computing Technological Educational Institute of Epirus Arta Greece Department of Electrical Engineering and Computer Technology University of Patras Greece Control Engineering Group Luleâ University of Technology Sweden

The ever-increasing number of electronic content stored in digital libraries requires a significant amount of effort in cataloguing and has led to self-deposit solutions where the authors submit and publish their own digital records. Even in self-deposit, going through the abstract and assigning subject terms or keywords is a time consuming and expensive process, yet crucial for the metadata quality of the record that affects retrieval. Therefore, an automatic, or even a semi-automatic process that can recommend topics for a new entry is of huge practical value. A system that can address that has to rely basically on two components, one component for efficiently representing the relevant information of the new document and one component for recommending an appropriate set of topics based on the representation of the previous stage. In this work, different candidate solutions for both components are investigated and compared. For the first stage both distributed Document to Vector (doc2vec) and conventional Bag of Words (BoW) components are employed, while for the latter two different transformation approaches from the field of multi-label classification are compared. For the comparison, a collection of Ph.D. abstracts (～19000 documents) from the MIT Libraries Dspace repository is used suggesting that different combinations can provide high quality solutions.

关键词： Feature extraction Predictive models Libraries Neural networks Classification algorithms Task analysis Training

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Aggregate experts knowledge in Fuzzy Cognitive Maps

Aggregate experts knowledge in Fuzzy Cognitive Maps

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IEEE International Conference on Fuzzy Systems

作者： Giovanni Mazzuto Maurizio Bevilacqua Chrysostomos Stylios Voula C. Georgopoulos Dipartimento Ingegneria Industriale e Scienze Matematiche Università Politecnica delle Marche Ancona Italy Laboratory of Knowledge and Intelligent Computing (KIC) Educational Institute of Epirus Arta Greece School Health and Welfare Professions TEI of Western Greece Patras Greece

In the last years, increased attention has been paid to the use of experts' opinions as concerned with the decisionmaking process and problem solving. Several approaches for aggregating them have been analysed but the literature review has highlighted that the experts' credibility in many cases is neglected. Thus, the present study aims at developing a novel approach for Fuzzy Cognitive Maps modelling, described in the literature as the unique methodology able to aggregate individuals knowledge and beliefs, combining a group decisionmaking technique, such as the Aggregation of Individual Evaluations one, and a structured credibility evaluation method according to the expertise areas of each expert. In particular, using a numerical example, the comparison between the traditional outcomes and those of the proposed method, highlights how the second one allows to obtain results more concordant with the single experts judgements than the first one, criticized of smoothing the experts opinion excessively.

关键词： Mathematical model Decision making Fuzzy cognitive maps Analytical models Aggregates Linguistics Computational modeling

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Remaining Useful Battery Life Prediction for UAVs based on Machine Learning

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IFAC-PapersOnLine 2017年第1期50卷 4727-4732页

作者： Mansouri S.S. Karvelis P. Georgoulas G. Nikolakopoulos G. Robotics Group Control Engineering Division of the Department of Computer Electrical and Space Engineering Luleå University of Technology Luleå SE-97187 Sweden Laboratory of Knowledge and Intelligent Computing Department of Computer Engineering Technological Educational Institute of Epirus Arta Greece

Unmanned Aerial Vehicles are becoming part of many industrial applications. The advancements in battery technologies played a crucial part for this trend. However, no matter what the advancements are, all batteries have a fixed capacity and after some time drain out. In order to extend the flying time window, the prediction of the time that the battery will no longer be able to support a flying condition is crucial. This in fact can be cast as a standard Remaining Useful Life prognostic problem, similarly encountered in many fields. In this article, the problem of Remaining Useful Life estimation of a battery, under different flight conditions, is tackled using four machine learning techniques: a linear sparse model, a variant of support vector regression, a multilayer perceptron and an advanced tree based algorithm. The efficiency of the overall proposed machine learning techniques, in the field of batteries prognostics, is evaluated based on multiple experimental data from different flight conditions. © 2017

关键词： Battery Machine Learning Prediction Remaining Useful Life UAVs

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Advancing the Scientific Method with Large Language Models: From Hypothesis to Discovery

arXiv

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arXiv 2025年

作者： Zhang, Yanbo Khan, Sumeer A. Mahmud, Adnan Yang, Huck Lavin, Alexander Levin, Michael Frey, Jeremy Dunnmon, Jared Evans, James Bundy, Alan Dzeroski, Saso Tegner, Jesper Zenil, Hector Allen Discovery Center Tufts University MedfordMA United States Living Systems Lab KAUST Thuwal Saudi Arabia Intelligent Infrastructure Team Network Rail United Kingdom Department for AI in Society Science and Technology Zuse Institute Berlin Germany NVIDIA Research United States Biological and Environmental Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia SDAIA-KAUST Center of Excellence in Data Science and Artificial Intelligence Thuwal23952 Saudi Arabia Pasteur Labs BrooklynNY United States Wyss Institute for Biologically Inspired Engineering Harvard University BostonMA United States Department of Chemistry University of Southampton University Road Hampshire Southampton United Kingdom Department of Biomedical Data Science Stanford University StanfordCA United States Knowledge Lab Department of Sociology University of Chicago IL United States Santa Fe Institute NM United States School of Informatics The University of Edinburgh United Kingdom Department of Knowledge Technologies Jožef Stefan Institute Ljubljana Slovenia Thuwal23955-6900 Saudi Arabia Unit of Computational Medicine Department of Medicine Center for Molecular Medicine Karolinska Institutet Karolinska University Hospital L8:05 StockholmSE-171 76 Sweden Thuwal23955-6900 Saudi Arabia Science for Life Laboratory Tomtebodavagen 23A SolnaSE-17165 Sweden Algorithmic Dynamics Lab Research Departments of Biomedical Computing and Digital Twins School of Biomedical Engineering and Imaging Sciences King’s College London United Kingdom King’s Institute for Artificial Intelligence London United Kingdom The Alan Turing Institute London United Kingdom Oxford Immune Algorithmics Oxford University Innovation London Institute for Healthcare Engineering London United Kingdom Cancer Interest Group Francis Crick Institute London United Kingdom

With recent Nobel Prizes recognising AI contributions to science, Large Language Models (LLMs) are transforming scientific research by enhancing productivity and reshaping the scientific method. LLMs are now involved in experimental design, data analysis, and workflows, particularly in chemistry and biology. However, challenges such as hallucinations and reliability persist. In this contribution, we review how Large Language Models (LLMs) are redefining the scientific method and explore their potential applications across different stages of the scientific cycle, from hypothesis testing to discovery. We conclude that, for LLMs to serve as relevant and effective creative engines and productivity enhancers, their deep integration into all steps of the scientific process should be pursued in collaboration and alignment with human scientific goals, with clear evaluation metrics. The transition to AI-driven science raises ethical questions about creativity, oversight, and responsibility. With careful guidance, LLMs could evolve into creative engines, driving transformative breakthroughs across scientific disciplines responsibly and effectively. However, the scientific community must also decide how much it leaves to LLMs to drive science, even when associations with ‘reasoning’, mostly currently undeserved, are made in exchange for the potential to explore hypothesis and solution regions that might otherwise remain unexplored by human exploration alone. Copyright © 2025, The Authors. All rights reserved.

关键词： Plants (botany)

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Fuzzy Cognitive Maps designing through large dataset and experts' knowledge balancing

Fuzzy Cognitive Maps designing through large dataset and exp...

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IEEE International Conference on Fuzzy Systems

作者： Giovanni Mazzuto Filippo E. Ciarapica Chrysostomos Stylios Voula C. Georgopoulos Dipartimento Ingegneria Industriale e Scienze Matematiche Università Politecnica delle Marche Ancona Italy Laboratory of Knowledge and Intelligent Computing (KIC) Educational Institute of Epirus Arta Greece INTERACT Lab School of Health & Welfare Professions Technological Educational Institute of Western Greece Patras Greece

This paper presents an extension of a hybrid method for modelling Fuzzy Cognitive Maps (FCMs), which combines human expert knowledge with existing recorded information and historical data. The proposed approach at first step extracts information from any type of dataset, extracts useful information and transforms it into knowledge in a form suitable to stracture a FCM. The proposed approach is still dependent on experts' knowledge but it support them by providing the information and supportive data inferred from the dataset. Moreover, the approach highlights, through a supportive numerical example, that experts' knowledge and information from the dataset cannot be considered separately, since their combined use compensates possible errors in knowledge. In particular, it can be used to model a complex Decision Support Systems, and it also allows to identify the accuracy of the considered models.

关键词： Computational modeling Mathematical model Fuzzy cognitive maps Data models Numerical models Complex systems Cognition

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