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INTRAGRO: A machine learning approach to predict future growth of trees under climate change

ECOLOGY AND EVOLUTION 2023年第10期13卷 e10626页

作者： Aryal, Sugam Griessinger, Jussi Dyola, Nita Gaire, Narayan Prasad Bhattarai, Tribikram Braeuning, Achim Friedrich Alexander Univ Erlangen Nurnberg Inst Geog Wetterkreuz 15 D-91058 Erlangen Bayern Germany Chinese Acad Sci Inst Tibetan Plateau Res State Key Lab Tibetan Plateau Earth Syst Resources Beijing Peoples R China Univ Quebec Chicoutimi Dept Sci Fondamentales Lab Ecosyst Terr Boreaux Chicoutimi PQ Canada Tribhuvan Univ Dept Environm Sci Patan Multiple Campus Lalitpur Nepal Tribhuvan Univ Cent Dept Biotechnol Kathmandu Nepal

The escalating impact of climate change on global terrestrial ecosystems demands a robust prediction of the trees' growth patterns and physiological adaptation for sustainable forestry and successful conservation efforts. Understanding these dynamics at an intra-annual resolution can offer deeper insights into tree responses under various future climate scenarios. However, the existing approaches to infer cambial or leaf phenological change are mainly focused on certain climatic zones (such as higher latitudes) or species with foliage discolouration during the fall season. In this study, we demonstrated a novel approach (INTRAGRO) to combine intra-annual circumference records generated by dendrometers coupled to the output of climate models to predict future tree growth at intra-annual resolution using a series of supervised and unsupervised machine learning algorithms. INTRAGRO performed well using our dataset, that is dendrometer data of P. roxburghii Sarg. from the subtropical mid-elevation belt of Nepal, with robust test statistics. Our growth prediction shows enhanced tree growth at our study site for the middle and end of the 21st century. This result is remarkable since the predicted growing season by INTRAGRO is expected to shorten due to changes in seasonal precipitation. INTRAGRO's key advantage is the opportunity to analyse changes in trees' intra-annual growth dynamics on a global scale, regardless of the investigated tree species, regional climate and geographical conditions. Such information is important to assess tree species' growth performance and physiological adaptation to growing season change under different climate scenarios.

关键词： dendrometer growing-season change intra-annual growth machine learning algorithm Pinus roxburghii sub-tropical Nepal

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machine learning Strategies for Analyzing Road Traffic Accident 15th

Machine Learning Strategies for Analyzing Road Traffic Accid...

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15th International Conference on Intelligent Human-Computer Interaction (IHCI)

作者： Gupta, Sumit Kumar, Awadhesh BHU Inst Sci Dept Comp Sci Varanasi Uttar Pradesh India BHU Dept Comp Sci MMV Varanasi Uttar Pradesh India

ISBN: (纸本)9783031538261;9783031538278

Road safety and accidents have been an important concern for the entire world and everyone is putting effort into resolving the long-standing problem of road safety and accidents. In every country on earth, there is traffic and reckless driving. This has a negative impact on a lot of pedestrians. They become victims, although having done nothing wrong. The number of traffic accidents is rising quickly due to the enormous increase in road cars. Accidents like these result in harm, impairment, and occasionally even fatalities. Numerous things like weather changes, sharp curves, and human error all contribute to the high number of traffic accidents. In this research paper various machine learning techniques such as, K Nearest Neighbors, Random Forest, Logistic Regression, Decision Tree, and XGBoost etc., are used to investigate why road traffic accidents occur in various nations throughout theworld. For evaluating and analyzing these algorithm several metrics, including precision, recall, accuracy and F1-Score are used to improve the performance of the dataset and predicts accuracy by approximately more than 85%.

关键词： machine learning algorithm Supervised learning Feature Analysis Road Accident metric parameter

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Translator attribution of Hongloumeng: using entropy-based features and machining learning algorithm

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DIGITAL SCHOLARSHIP IN THE HUMANITIES 2025年第1期40卷 138-150页

作者： Hu, Ruitao Wang, Gui Shao, Bin Zhejiang Univ Sch Int Studies 866 Yuhangtang Rd Hangzhou 310058 Zhejiang Peoples R China

This study utilized machine learning algorithms and entropy-based features to identify translators of two English translations of Hongloumeng, a great classical Chinese novel written in the mid-18th century. The translations under examination were completed, respectively, by David Hawkes and the Yangs (Yang Hsien-yi and Gladys Yang). Two feature sets were extracted as input for the identification of translator styles: wordform features (wordform unigrams, bigrams, and trigrams) and part-of-speech (POS) features (POS unigrams, bigrams, and trigrams). Additionally, four machine learning classifiers were tested: linear support vector machines (SVMs), linear discriminant analysis (LDA), random forest (RF), and multilayer perceptron (MLP). Analysis of feature importance and SHAP value identified the most influential features within each classifier. Results showed that LDA achieved the best performance, with 81 per cent accuracy in distinguishing between translations, showing promise for translator identification. In contrast, MLP struggled to reliably differentiate between translations, achieving only 50 per cent accuracy. Furthermore, POS features had the greatest influence in SVM and LDA, while wordform features dominated in RF. SHAP analysis revealed that Hawkes' translation tended to exhibit higher POS unigram and lower POS trigram entropy compared to the Yangs'. This increased contribution of POS unigrams and trigrams suggests a link to explicitation differences in translation. In summary, the combination of machine learning and entropy-based stylometric features shows potential for automatic translator identification and analysis.

关键词： Hongloumeng translator attribution entropy machine learning algorithm

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Gelatin and zinc ion-cooperated triple crosslinked hydrogels with high mechanical properties and ultrasensitivity for multimodal sensing and handwriting recognition

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INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES 2025年第Pt 2期304卷 140869页

作者： Dong, Hao Liu, Chuanliu Zhang, Xu Si, Dongyu Luo, Yiming Jia, Jiale Han, Menglu Su, Ying Zhou, Hanyu Zeng, Wei Tianjin Univ Sci & Technol Sch Mat & Chem Engn Tianjin 300457 Peoples R China Tianjin Univ Sci & Technol State Key Lab Biobased Fiber Mfg Technol Tianjin 300457 Peoples R China Tianjin Univ Sci & Technol Coll Artificial Intelligence Tianjin 300457 Peoples R China Tianjin Univ Sci & Technol Sch Elect Informat & Automat Tianjin 300457 Peoples R China

With the rapid development of flexible electronics technology, high-performance flexible sensors have shown great potential in wearable devices and human-computer interaction fields. In this study, a hydrogel (PMAGZ) reinforced by gelatin and Zn2+ was prepared using a one-pot method, which formed a triple-bonded cross-linked network structure through covalent, hydrogen, and ligand bonds to exhibit excellent mechanical properties and sensing characteristics, and can be applied to multimodal sensors and handwriting recognition. The introduction of gelatin and Zn2+ strengthens the cross-linked structure inside the hydrogel, which can effectively improve the tensile strength, strain, and toughness of the hydrogel. Additionally, the addition of ethylene glycol and lithium chloride endowed the hydrogel with good frost resistance and electrical conductivity. The PMAGZ hydrogel sensor has a fast response time, high sensitivity, wide sensing range, and excellent fatigue resistance, and is capable of accurately monitoring human movements and handwriting information. Combined with machine learning algorithms (ML), the sensor can effectively recognize different handwritten letters with an accuracy of 99.8 %. This study provides a new approach for developing high-performance, multifunctional flexible sensors, which have broad application prospects in flexible wearable devices and human-computer interaction fields.

关键词： Gelatin Triple bonding Multifunctional sensor machine learning algorithm Multimodal recognition

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Time-resolved 3D CT with failure physics (5D CT) for quantifying internal defect evolution behaviors of criticality safety AM components

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ENGINEERING FRACTURE MECHANICS 2025年 319卷

作者： Wu, Shengchuan Southwest Jiaotong Univ State Key Lab Rail Transit Vehicle Syst 111 Sect 1 North Second Ring Rd Chengdu 610031 Peoples R China

Additive manufacturing (AM) processes give rise to complex internal architectures, which in turn lead to anisotropic mechanical properties. To identify failure origins, various microscopic instruments can well gather damage features. Nevertheless, these slice-based methods offer limited two-dimensional (2D) information, making it difficult to connect long-term service behaviors to internal events. To tackle this drawback, damage constitutive-informed mechanics enables the modeling of critical damage processes. For three-dimensional (3D) objects, cutting-edge techniques, such as time lapse X-ray tomography and neutron tomography/scattering with sample environments, can non-destructively probe those history events beneath the surface of the AM components. By combining powerful computers, machine learning algorithms, and a vast number of radiographs and images, we can reconstruct precise 3D renderings with internal structures. Additionally, to visualize damage features and their evolution, numerical modeling can be employed under real conditions, quantifying critical failure events and then tailoring the fatigue resistance of AM materials. This work formulates a framework that integrates time-resolved 3D computed tomography (CT) with failure physics (5D CT). The ultimate goal of novel tomography mechanics is to trace vector damage fields, rather than solely relying on qualitative 2D images.

关键词： Internal defects and cracks Additive manufacturing X-ray and neutron tomography Image-based finite element machine learning algorithm

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Unobtrusive machine learning based leg position detection during seated office work

Unobtrusive machine learning based leg position detection du...

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IEEE International Symposium on Medical Measurements and Applications (MeMeA)

作者： Ong, Linda Cao, Ming Verkerke, G. J. (Bart) Lamoth, C. J. C. (Claudine) Wilhelm, Elisabeth Univ Groningen Engn & Technol Inst Groningen Netherlands Univ Groningen Univ Med Ctr Groningen Dept Rehabil Med Groningen Netherlands Univ Groningen Univ Med Ctr Groningen Dept Human Movement Sci Groningen Netherlands

ISBN: (纸本)9798350308006;9798350307993

Musculoskeletal disorders (MSD) affect more than 1.63 billion people worldwide. Office workers are at high risk of developing MSD due to prolonged sitting during office work. Technical aids that provide users with insights into their sitting behaviour are useful for the prevention of MSD. Despite the fact that leg position is known to influence spine curvature, most position detection devices focus on upper body posture. In comparison, we recorded contact force with the chair using pressure sensors with the additional information from videos that showed the sitting position in 30 participants. Each participant was recorded during 5 working days resulting in a median recording time of 22 hours and 53 minutes. The active number of sensors, mean value and area under the curve of sensors in specific areas, feature extraction with convolution filters, and center of pressure were extracted from the force sensor values. An XGboost classification algorithm was trained on these features to discriminate between four different leg positions and the absence of a user. This algorithm obtained an overall accuracy of 73% on the test set consisting of 6 participants. The f1-scores for the classes 'away', 'no cross', 'knee cross', and 'ankle cross' were 0.96, 0.54, 0.76, and 0.44 respectively. The class legs on the chair, which rarely occurred in the monitored population, were mistaken for 'no cross' and could not be identified correctly in the test set. A second XGboost classifier was able to differentiate between symmetric and asymmetric sitting leg positions and away with a weighted accuracy of 85%. Overall pressure mats are a promising technology for observing common leg postures in office environments.

关键词： Decision support system machine learning algorithm Monitoring Musculoskeletal system Occupational Health Predictive model Pressure sensors

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Harnessing Multi-satellite Remote Sensing Data and machine learning for Flood Risk Assessment in Nam Ngum River Basin, Lao PDR

Harnessing Multi-satellite Remote Sensing Data and Machine L...

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SPIE Future Sensing Technologies Conference

作者： Mangkhaseum, Sackdavong Duwal, Sunil Bhattarai, Yogesh Hanazawa, Akitoshi Kyushu Inst Technol Dept Elect & Space Syst Engn Kitakyushu Fukuoka Japan Khwopa Coll Engn Dept Civil Engn Bhaktapur Nepal

ISBN: (数字)9781510674905

ISBN: (纸本)9781510674905;9781510674899

The study focuses on flood susceptibility in the Nam Ngum River Basin, Lao PDR, an area prone to annual flooding due to monsoons and rainstorms. Flooding in this region significantly threatens human life, causes economic losses, and damages communities and agriculture. The study employs advanced remote sensing and machine learning techniques, including random forest (RF), support vector machine (SVM), and artificial neural networks (ANN), to address these issues and create detailed flood susceptibility maps. The machine learning models used historical flood data, Sentinel-1 SAR imagery from 2018 to 2020, and open-source flood data for training and validation. Eleven flood factors were considered. With 776 samples, 70% were trained, and 30% tested the model. Flood susceptibility map accuracy is assessed using statistical techniques such as multicollinearity, Kappa index, and area under the curve of receiver operating characteristics (AUROC). The generated flood susceptibility map is used to analyze the possible effect on the different land use/land cover classes and populations. RF outperforms SVM and ANN, achieving higher accuracy based on Receiver Operating Characteristics. The resulting flood susceptibility map reveals that 25-44% of the basin area is highly susceptible, predominantly in low-elevation and low-slope regions. Likewise, 85 to 90% of the people are highly vulnerable to flooding within 260 to 280 km2 of built-up area. The study proposes a new approach to using machine learning and readily available remote sensing data for flood susceptibility mapping. The findings of this study provide essential insights for policymakers, aiding in disaster risk reduction and facilitating sustainable development planning in Lao PDR.

关键词： Flood susceptibility modeling machine learning algorithm remote sensing risk assessment Nam Ngum River Basin Laos

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Identification of key genes associated with oxidative stress in ischemic stroke via bioinformatics integrated analysis

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BMC NEUROSCIENCE 2025年第1期26卷 1-12页

作者： Li, Gaiyan Cheng, Yu Ding, Shanshan Zheng, Qianyun Kuang, Lanqiong Zhang, Ying Zhou, Ying Shanghai Xuhui Cent Hosp Dept Rehabil Shanghai Peoples R China Shanghai Xuhui Cent Hosp Dept Image Shanghai Peoples R China Shanghai Xuhui Cent Hosp Dept Gen Practice Shanghai Peoples R China

BackgroundIschemic stroke (IS) is a common cerebrovascular disease. Although the formation of atherosclerosis, which is closely related to oxidative stress (OS), is associated with stroke-related deaths. However, the role of OS in IS is ***-related key genes were obtianed by overlapping the differentially expressed genes (DEGs) between IS and normal control (NC) specimens, IS-related genes, and OS-related genes. Then, we investigated the mechanism of action of key genes. Subsequently, protein-protein interaction (PPI) network and machine learning algorithms were utilized to excavate feature genes. In addition, the network between feature genes and microRNAs (miRNAs) was established to investigate the regulatory mechanism of feature genes. Finally, quantitative PCR (qPCR) was utilized to validate the expression of feature genes with blood *** total of 42 key genes related to OS were acquired. Enrichment analysis indicated that the key genes were associated with oxidative stress, reactive oxygen species, lipid and atherosclerosis, and cell migration-related pathways. Then, 6 feature genes (HSPA8, NCF2, FOS, KLF4, THBS1, and HSPA1A) related to OS were identified for IS. Besides, 6 feature genes and 255 miRNAs were utilized to establish a feature genes-miRNA network which contained 261 nodes and 277 edges. At last, qPCR results revealed that there was a trend for higher expression of FOS, KLF4, and HSPA1A in IS specimens than in NC specimens. Additionally, HSPA8 expression was significantly decreased in the IS specimens, which was consistent with the findings of the GEO database *** conclusion, 6 feature genes (HSPA8, NCF2, FOS, KLF4, THBS1, and HSPA1A) related to OS were mined by bioinformatics analysis, which might provide a new insights into the evaluation and treatment of *** trial number: Not *** conclusion, 6 feature genes (HSPA8, NCF2, FOS, KLF4, THBS1, and HSPA1A) related to OS were mined

关键词： Ischemic stroke Oxidative stress machine learning algorithm WGCNA

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machine learning-based mortality prediction models using national liver transplantation registries are feasible but have limited utility across countries

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AMERICAN JOURNAL OF TRANSPLANTATION 2023年第1期23卷 64-71页

作者： Ivanics, Tommy So, Delvin Claasen, Marco P. A. W. Wallace, David Patel, Madhukar S. Gravely, Annabel Choi, Woo Jin Shwaartz, Chaya Walker, Kate Erdman, Lauren Sapisochin, Gonzalo Univ Hlth Network Toronto Multi Organ Transplant Program Toronto ON Canada Henry Ford Hosp Dept Surg Detroit MI USA Uppsala Univ Dept Surg Sci Akad Sjukhuset Uppsala Sweden Hosp Sick Children Ctr Computat Med Toronto ON Canada Univ Med Ctr Rotterdam Erasmus MC Transplant Inst Dept Surg Div HPB & Transplant Surg Rotterdam Netherlands Kings Coll Hosp NHS Fdn Trust London Sch Hyg & Trop Med Dept Hlth Serv Res & Policy London England Kings Coll Hosp NHS Fdn Trust Inst Liver Studies London England Guys & St Thomas NHS Fdn Trust Dept Nephrol & Transplantat London England Univ Texas Southwestern Med Ctr Div Surg Transplantat Dept Surg Dallas TX USA 585 Univ Ave 11PMB184 Toronto ON M5G 2N2 Canada

Many countries curate national registries of liver transplant (LT) data. These registries are often used to generate predictive models;however, potential performance and transferability of these models remain unclear. We data from 3 national registries and developed machine learning algorithm (MLA)-based models to predict 90 post-LT mortality within and across countries. Predictive performance and external validity of each model assessed. Prospectively collected data of adult patients (aged >= 18 years) who underwent primary LTs between January 2008 and December 2018 from the Canadian Organ Replacement Registry (Canada), National Service Blood and Transplantation (United Kingdom), and United Network for Organ Sharing (United were used to develop MLA models to predict 90-day post-LT mortality. Models were developed using each registry individually (based on variables inherent to the individual databases) and using all 3 registries combined iables in common between the registries [harmonized]). The model performance was evaluated using area the receiver operating characteristic (AUROC) curve. The number of patients included was as follows: Canada, = 1214;the United Kingdom, n = 5287;and the United States, n = 59,558. The best performing MLA-based model was ridge regression across both individual registries and harmonized data sets. Model performance diminished from individualized to the harmonized registries, especially in Canada (individualized ridge: AUROC, 0.74;range, 0.73-0.74;harmonized: AUROC, 0.68;range, 0.50-0.73) and US (individualized ridge: AUROC, range, 0.70-0.71;harmonized: AUROC, 0.66;range, 0.66-0.66) data sets. External model performance countries was poor overall. MLA-based models yield a fair discriminatory potential when used within individual databases. However, the external validity of these models is poor when applied across countries. Standardization of registry-based variables could facilitate the added value of MLA-based models in informing de

关键词： machine learning algorithm international liver registry liver transplantation outcome prediction

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Gully erosion spatial modelling: Role of machine learning algorithms in selection of the best controlling factors and modelling process

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Geoscience Frontiers 2020年第6期11卷 2207-2219页

作者： Hamid Reza Pourghasemi Nitheshnirmal Sadhasivam Narges Kariminejad Adrian L.Collins Department of Natural Resources and Environmental Engineering College of AgricultureShiraz UniversityShirazIran Department of Geography School of Earth ScienceBharathidasan UniversityTiruchirappalli620024Tamil NaduIndia Department of Watershed and Arid Zone Management Gorgan University of Agricultural Sciences and Natural ResourcesGorganIran Sustainable Agriculture Sciences Rothamsted ResearchNorth WykeOkehamptonDevonEX202SBUK

This investigation assessed the efficacy of 10 widely used machine learning algorithms(MLA)comprising the least absolute shrinkage and selection operator(LASSO),generalized linear model(GLM),stepwise generalized linear model(SGLM),elastic net(ENET),partial least square(PLS),ridge regression,support vector machine(SVM),classification and regression trees(CART),bagged CART,and random forest(RF)for gully erosion susceptibility mapping(GESM)in *** location of 462 previously existing gully erosion sites were mapped through widespread field investigations,of which 70%(323)and 30%(139)of observations were arbitrarily divided for algorithm calibration and *** controlling factors for gully erosion,namely,soil texture,annual mean rainfall,digital elevation model(DEM),drainage density,slope,lithology,topographic wetness index(TWI),distance from rivers,aspect,distance from roads,plan curvature,and profile curvature were ranked in terms of their importance using each *** MLA were compared using a training dataset for gully erosion and statistical measures such as RMSE(root mean square error),MAE(mean absolute error),and *** on the comparisons among MLA,the RF algorithm exhibited the minimum RMSE and MAE and the maximum value of R-squared,and was therefore selected as the best *** variable importance evaluation using the RF model revealed that distance from rivers had the highest significance in influencing the occurrence of gully erosion whereas plan curvature had the least *** to the GESM generated using RF,most of the study area is predicted to have a low(53.72%)or moderate(29.65%)susceptibility to gully erosion,whereas only a small area is identified to have a high(12.56%)or very high(4.07%)*** outcome generated by RF model is validated using the ROC(Receiver Operating Characteristics)curve approach,which returned an area under the curve(AUC)of 0.985,proving the excellent forecasting ability of the model

关键词： machine learning algorithm Gully erosion Random forest Controlling factors Variable importance

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