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Integrated data-model-knowledge representation for natural resource entities

International Journal of Digital Earth 2022年第1期15卷 653-678页

作者： Yulin Ding Zhaowen Xu Qing Zhu Hankan Li Yan Luo Ying Bao Lingjun Tang Sen Zeng faculty of Geosciences and Environmental Engineering Southwest Jiaotong UniversityChengduPeople's Republic of China Municipal planning & Land Estate Information Center Shenzhen Municipal Bureau of Planning and Natural ResourcesShenzhenPeople's Republic of China Information Center Department of Natural Resources of Sichuan ProvinceChengduPeople's Republic of China

The unified management and planning of national or provincial natural resources distributed both aboveground and underground have become increasingly *** depictions of natural resource elements and their interactions are key to achieving integrated and systematic management of natural ***,current spatiotemporal data models are based only on data descriptions,attribute records,and other model knowledge of a more general basis,without intuitively describing relationships between these elements and natural *** paper,therefore,proposes an integrated data-model-knowledge representation model to explicitly describe the time,space,and interaction of natural resource entities through an integrated knowledge ***,this study constructs a conceptual model using the aspects of semantics,scale,and data-model-knowledge,thereby explicitly describing the relationships of natural ***,a logical model of natural resource representation is proposed,that is integrated with time,space,attributes,and ***,taking the management of water resources as an example,this paper realizes the meticulous presentation of the levels of detail and rich semantic relations of natural resource *** findings of this study lay the foundation for a more efficient,precise,and lucid perception of the distribution laws and complicated interactional relationships of natural resources,both aboveground and underground.

关键词： Natural resources data-model-knowledge interactional relationships

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Presentation of machine learning methods to determine the most important factors affecting road traffic accidents on rural roads

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Frontiers of Structural and Civil Engineering 2022年第5期16卷 657-666页

作者： Hamid MIRZAHOSSEIN Milad SASHURPOUR Seyed Mohsen HOSSEINIAN Vahid Najafi Moghaddam GILANI Civil–Transportation planning Department Faculty of Technical and EngineeringImam Khomeini International University(IKIU)Qazvin 34148-96818Iran School of Civil Engineering Iran University of Science and Technology(IUST)Tehran 13114-16846Iran

The purpose of this research was to develop statistical and intelligent models for predicting the severity of road traffic accidents(RTAs)on rural *** Logistic Regression(MLR)was used to predict the likelihood of *** more accurate prediction,Multi-Layer Perceptron(MLP)and Radius Basis Function(RBF)neural networks were *** indicated that in MLR,the model obtained from the backward method with the correct percent of 84.7%and R2 value of 0.893 was the best method for predicting the likelihood of ***,MLR showed that the variables of not paying attention to the front not paying attention to the frontroad ahead,followed byand then vehicle-motorcycle/bike accidents were the greatest *** the models,MLP had a better performance,so that the prediction accuracy of MLR,MLP,and RBF were 84.7%,96.7%,and 92.1%,*** model,due to higher accuracy,showed that the variable of reason of accident had the highest effect on the prediction of accidents,and considering MLR results,the variables of not paying attention to the front and then vehicle-motorcycle/bike accidents had the most influence on the occurrence of ***,motorcyclists and cyclists are more prone to accidents,and appropriate solutions should be adopted to enhance their safety.

关键词： safety rural accidents multiple logistic regression artificial neural networks

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Landscape of global urban environmental resistome and its association with local socioeconomic and medical status

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Science China(Life Sciences) 2024年第6期67卷 1292-1301页

作者： Jun Wu Yige Hu Michael H Perlin David Danko Jun Lu Manuela Oliveira Johannes Werner Maria M Zambrano Maria A Sierra Olayinka O Osuolale Paweł Łabaj Nicolás Rascovan Nur Hazlin Hazrin-Chong Soojin Jang Haruo Suzuki Marina Nieto-Caballero Bharath Prithiviraj Patrick K H Lee Agnieszka Chmielarczyk Anna Różańska Yongxiang Zhao Lan Wang Christopher E Mason Tieliu Shi Center for Bioinformatics and Computational Biology and the Institute of Biomedical SciencesSchool of Life SciencesEast China Normal UniversityShanghai 200241China Department of Biology Program on Disease EvolutionUniversity of LouisvilleLouisville 40292USA Weill Cornell Medicine New York 10065USA The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine New York 10065USA Department of Pulmonary Medicine Shanghai Chest HospitalShanghai Jiao Tong UniversityShanghai 200025China i3S-Instituto de Investiga玢o e Inova玢o em Saúde Universidade do PortoPorto 4050-290Portugal Ipatimup-Instituto de Patologia e Imunologia Molecular da Universidade do Porto Porto 4200-465Portugal Departamento de Biologia Faculdade de CiênciasUniversidade do PortoPorto 4050-290Portugal High Performance and Cloud Computing Group Zentrum für Datenverarbeitung(ZDV)Eberhard Karls University of TübingenW鋍hterstra遝7672074TübingenGermany Corporación Corpogen Research Center Bogotá110311Colombia Applied Environmental Metagenomics and Infectious Diseases Research(AEMIDR) Department of Biological SciencesElizade UniversityIlara-Mokin 340271Nigeria Maopolska Centre of Biotechnology Jagiellonian UniversityKraków 30-005Poland Aix-Marseille Université Mediterranean Institute of OceanologyUniversitéde ToulonCNRSIRDUM 110Marseille 83041France Department of Biological Sciences and Biotechnology Faculty of Science and TechnologyUniversiti Kebangsaan Malaysia43600 UKM BangiSelangor Institut Pasteur Korea Seoul 13488Republic of Korea faculty of Environment and Information Studies Keio UniversityFujisawaKanagawa 252-0882Japan University of Colorado at Boulder CivilEnvironmental and Architectural DepartmentBoulder 80303USA Brooklyn College of the City University of New York Brooklyn 11210USA School of Energy and Environment City University of Hong KongHong Kong 999077China Department of Microbiology Faculty of MedicineJagiellonian UniversityKraków 30-005Poland Biological Targeting Diagnosis and The

Antimicrobial resistance(AMR)poses a critical threat to global health and development,with environmental factors—particularly in urban areas—contributing significantly to the spread of antibiotic resistance genes(ARGs).However,most research to date has been conducted at a local level,leaving significant gaps in our understanding of the global status of antibiotic resistance in urban *** address this issue,we thoroughly analyzed a total of 86,213 ARGs detected within 4,728 metagenome samples,which were collected by the Meta SUB International Consortium involving diverse urban environments in 60 cities of 27 countries,utilizing a deep-learning based *** findings demonstrated the strong geographical specificity of urban environmental resistome,and their correlation with various local socioeconomic and medical *** also identified distinctive evolutionary patterns of ARG-related biosynthetic gene clusters(BGCs)across different countries,and discovered that the urban environment represents a rich source of novel *** study provides a comprehensive overview of the global urban environmental resistome,and fills a significant gap in our knowledge of large-scale urban antibiotic resistome analysis.

关键词： urban environment metagenome antibiotic resistance resistance diversity biosynthetic gene clusters

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Advancing Early Detection of Colorectal Adenomatous Polyps via Genetic Data Analysis: A Hybrid Machine Learning Approach

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Journal of Computer and Communications 2024年第7期12卷 23-38页

作者： Ahmed S. Maklad Mohamed A. Mahdy Amer Malki Noboru Niki Abdallah A. Mohamed Computer Science Department College of Computer Science and Engineering in Yanbu Taibah University Medina Saudi Arabia Information Systems Department Faculty of Computers and Artificial Intelligence Beni-Suef University Beni-Suif Egypt Computer Science Department Faculty of Computers and Artificial Intelligence Beni-Suef University Beni-Suif Egypt Institute of Advanced Science and Technology Tokushima University Tokushima Japan Information Systems Department College of Computer Science and Engineering in Yanbu Taibah University Medina Saudi Arabia Mathematics and Computer Science Department Faculty of Science Menoufia University Menoufia Egypt

In this study, a hybrid machine learning (HML)-based approach, incorporating Genetic data analysis (GDA), is proposed to accurately identify the presence of adenomatous colorectal polyps (ACRP) which is a crucial early detector of colorectal cancer (CRC). The present study develops a classification ensemble model based on tuned hyperparameters. Surpassing accuracy percentages of early detection approaches used in previous studies, the current method exhibits exceptional performance in identifying ACRP and diagnosing CRC, overcoming limitations of CRC traditional methods that are based on error-prone manual examination. Particularly, the method demonstrates the following CRP identification accuracy data: 97.7 ± 1.1, precision: 94.3 ± 5, recall: 96.0 ± 3, F1-score: 95.7 ± 4, specificity: 97.3 ± 1.2, average AUC: 0.97.3 ± 0.02, and average p-value: 0.0425 ± 0.07. The findings underscore the potential of this method for early detection of ACRP as well as clinical use in the development of CRC treatment planning strategies. The advantages of this approach are highly expected to contribute to the prevention and reduction of CRC mortality.

关键词： Colorectal Adenoma Detection Colorectal Cancer Diagnosis Hybrid Machine Learning Genetics Analysis

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Efficient computation of Hash Hirschberg protein alignment utilizing hyper threading multi-core sharing technology

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CAAI Transactions on Intelligence Technology 2022年第2期7卷 278-291页

作者： Muhannad Abu-Hashem Adnan Gutub Department of Geomatics Faculty of Architecture and PlanningKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Engineering College of Computer&Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia

Due to current technology enhancement,molecular databases have exponentially grown requesting faster efficient methods that can handle these amounts of huge ***-fore,Multi-processing CPUs technology can be used including physical and logical processors(Hyper Threading)to significantly increase the performance of ***,sequence comparison and pairwise alignment were both found contributing significantly in calculating the resemblance between sequences for constructing optimal *** research used the Hash Table-NGram-Hirschberg(HT-NGH)algo-rithm to represent this pairwise alignment utilizing hashing *** authors propose using parallel shared memory architecture via Hyper Threading to improve the performance of molecular dataset protein pairwise *** proposed parallel hyper threading method targeted the transformation of the HT-NGH on the datasets decomposition for sequence level efficient utilization within the processing units,that is,reducing idle processing unit *** authors combined hyper threading within the multicore architecture processing on shared memory utilization remarking perfor-mance of 24.8%average speed up to 34.4%as the highest boosting *** benefit of this work improvement is shown preserving acceptable accuracy,that is,reaching 2.08,2.88,and 3.87 boost-up as well as the efficiency of 1.04,0.96,and 0.97,using 2,3,and 4 cores,respectively,as attractive remarkable results.

关键词： computational biology high-performance computing Hyper Threading pairwise sequence alignment parallel design sequence alignment shared-memory

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City Digital Twins: their maturity level and differentiation from 3D city models

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Big Earth Data 2023年第1期7卷 1-36页

作者： Homa Masoumi Sara Shirowzhan Paria Eskandarpour Christopher James Pettit Department of Urban and Regional planning Shahid Beheshti UniversityTehranIran School of Built Environment UNSW SydneySydneyAustralia faculty of Information Technology Monash UniversityClaytonVICAustralia faculty of Arts Design and ArchitectureCity Futures Research CentreUNSW SydneyNSWAustralia

The emerging field of City Digital Twins has advanced in recent years with the help of digital infrastructure and technologies connected to the Internet of Things(IoT).However,the evolution of this field has been so fast that a gap has opened in relation to systematic reviews of the relevant literature and the maturation of City Digital Twins on an urban *** work bridges this gap by highlighting maturity in the *** conducted a systematic literature review with bibliometric and content analysis of 41 selected papers published in Web of Science and Scopus data-bases,covering five areas:data types and sources,case studies,applied technologies and methods,maturity spectrum,and *** on maturity indicators,the majority of the reviewed studies(90%)were at initial to medium stages of maturity(up to element 3),most of them focused on 3D modelling,monitoring and ***,digital twins cannot be limited to 3D models,monitoring and visualisation,for they can be developed to include two-directional interactions between humans and *** a high level of maturity,which was not found in the reviewed studies,requires advanced technologies and methods such as cloud computing,artificial intelligence,BIM and *** also found that further studies are essential if the field is to handle the complex urban challenges of multidisciplinary digital *** City Digital Twins extend by definition beyond mere 3D city modelling,some studies involving 3D city models still refer to their subjects as City Digital *** the research gaps we identified,we’d like to highlight the need for near-real-time data analytics algorithms,which could furnish City Digital Twins with big data *** opportunities include public participation capabilities to increase social collaboration,integrating BIM and GIS technologies and improving storage and computation infrastructure.

关键词： City Digital Twins big data IoT artificial intelligence city information modelling

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Novel Hybrid X GBoost Model to Forecast Soil Shear Strength Based on Some Soil Index Tests

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Computer Modeling in Engineering & Sciences 2023年第9期136卷 2527-2550页

作者： Ehsan Momeni Biao He Yasin Abdi Danial Jahed Armaghani Department of Civil Engineering Faculty of EngineeringLorestan UniversityKhorramabad***Iran Department of Civil Engineering Faculty of EngineeringUniversiti MalayaKuala Lumpur50603Malaysia Department of Geology Faculty of SciencesLorestan UniversityKhorramabad***Iran Department of Urban planning Engineering Networks and SystemsInstitute of Architecture and ConstructionSouth Ural State UniversityChelyabinsk454080Russia

When building geotechnical constructions like retaining walls and dams is of interest,one of the most important factors to consider is the soil’s shear strength *** study makes an effort to propose a novel predictive model of shear *** study implements an extreme gradient boosting(XGBoost)technique coupled with a powerful optimization algorithm,the salp swarm algorithm(SSA),to predict the shear strength of various *** do this,a database consisting of 152 sets of data is prepared where the shear strength(τ)of the soil is considered as the model output and some soil index tests(e.g.,dry unit weight,water content,and plasticity index)are set as model *** is designed and tuned using both effective parameters of XGBoost and SSA,and themost accuratemodel is introduced in this *** theSSA-XGBoostmodel is assessedbased on the coefficient of determination(R2)and variance account for(VAF).Overall,the obtained values of R^(2) and VAF(0.977 and 0.849)and(97.714%and 84.936%)for training and testing sets,respectively,confirm the workability of the developed model in forecasting the soil shear *** investigate the model generalization,the prediction performance of the model is tested for another 30 sets of data(validation data).The validation results(e.g.,R^(2) of 0.805)suggest the workability of the proposed ***,findings suggest that when the shear strength of the soil cannot be determined directly,the proposed hybrid XGBoost-SSA model can be utilized to assess this parameter.

关键词： Predictive model salp swarm algorithm soil index tests soil shear strength XGBoost

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Resilience of Wengding,the Phoenix Wa traditional village

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Geography and Sustainability 2023年第4期4卷 372-378页

作者： Yishu Wang Zhigang Song Qiaoge Yang faculty of Architecture and City planning Kunming University of Science and TechnologyKunmingChina faculty of Civil Engineering and Mechanics Kunming University of Science and TechnologyKunmingChina Power China Kunming Engineering Corporation Limited KunmingChina

The village of Wengding,ancestral home of the Wa ethnic group,considered as China’s last primitive tribe,suf-fered near-complete extinction due to a fire incident in *** records indicate that stilt-style buildings,also in Wengding,are highly susceptible to ***,this has not hindered the long-term existence of these distinctive architectural *** study analyses the reasons for the sustained existence of the traditional building forms in Wengding,from the perspective of *** findings indicate that the requirements of the traditional nomadic civilization for rapid construction of dwellings,the communal consciousness toward dis-aster resistance,and the implementation of spatially oriented fire protection measures collectively account for the swift recovery and reconstruction observed in Wengding village after the *** aspects are tangible manifestations of the capacity of the village for ***,resilience is essential for the sustainability of such traditional villages in the long term.

关键词： Wengding village Disaster resilience Sustainability Dwelling characteristics Public life Spatial form

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Predicting 3D Radiotherapy Dose-Volume Based on Deep Learning

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Intelligent Automation & Soft Computing 2024年第2期39卷 319-335页

作者： Do Nang Toan Lam Thanh Hien Ha Manh Toan Nguyen Trong Vinh Pham Trung Hieu Institute of Information Technology Vietnam Academy of Science and TechnologyHanoi10072Vietnam faculty of Information Technology Lac Hong UniversityBien HoaDong Nai76120Vietnam

Cancer is one of the most dangerous diseaseswith *** of the principal treatments is radiotherapy by using radiation beams to destroy cancer cells and this workflow requires a lot of experience and skill from doctors and *** our study,we focused on the 3D dose prediction problem in radiotherapy by applying the deeplearning approach to computed tomography(CT)images of cancer *** image data has more complex characteristics than normal image data,and this research aims to explore the effectiveness of data preprocessing and augmentation in the context of the 3D dose prediction *** proposed four strategies to clarify our hypothesis in different aspects of applying data preprocessing and *** strategies,we trained our custom convolutional neural network model which has a structure inspired by the U-net,and residual blocks were also applied to the *** output of the network is added with a rectified linear unit(Re-Lu)function for each pixel to ensure there are no negative values,which are absurd with radiation *** experiments were conducted on the dataset of the Open Knowledge-Based planning Challenge which was collected from head and neck cancer patients treatedwith radiation *** results of four strategies showthat our hypothesis is rational by evaluating metrics in terms of the Dose-score and the Dose-volume histogram score(DVH-score).In the best training cases,the Dose-score is 3.08 and the DVH-score is *** addition,we also conducted a comparison with the results of another study in the same context of using the loss function.

关键词： CT image 3D dose prediction data preprocessing augmentation

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Projected impacts of climate change and anthropogenic effects on habitat distribution of endangered Javan Hawk-Eagle in Indonesia

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Geography and Sustainability 2024年第2期5卷 241-250页

作者： Syartinilia Aryo Adhi Condro Satoshi Tsuyuki Department of Landscape Architecture Faculty of AgricultureIPB University(Bogor Agricultural University)Bogor 16680Indonesia International Research Institute for Environment and Climate Change-IPB University Collaborative Research CentreBogor 16128Indonesia Rainforest Alliance DenpasarBali 80227Indonesia Graduate School of Agricultural and Life Sciences The University of TokyoTokyo 113-8657Japan

Changing climate will jeopardize biodiversity,particularly the geographic distribution of endemic *** such species is the Javan Hawk-Eagle(JHE,Nisaetus bartelsi),a charismatic raptor found only on Java Island,***,it is crucial to develop an appropriate conservation strategy to preserve the *** niche modeling is considered a valuable tool for designing conservation plans for the *** provide an ecological niche modeling approach and transfer its model to future climate scenarios for the *** utilize various machine learning algorithms under sustainability and business-as-usual(BAU)scenarios for ***,we investigate the conservation vulnerability of the JHE,capturing multifaceted pressures on the species from climate dissimilarities and human disturbance *** study reveals that the ensemble model performs exceptionally well,with temperature emerging as the most critical factor affecting the JHE *** finding indicates that climate change will have a significant impact on the JHE *** results suggest that the JHE distribution will likely decrease by 28.41%and 40.16%from the current JHE distribution under sustainability and BAU scenarios,***,our study reveals high-potential refugia for future JHE,covering 7,596 km^(2)(61%)under the sustainability scenario and only 4,403 km^(2)(35%)under the BAU ***,effective management and planning,including habitat restoration,refugia preservation,habitat connectivity,and local community inclusivity,should be well-managed to achieve JHE conservation targets.

关键词： Climate change Ecological niche Ensemble model Refugia Javan Hawk-Eagle

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