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Reconstruction of high spatial resolution surface air temperature data across China: A new geo-intelligent multisource data-based machine learning technique

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SCIENCE OF THE TOTAL ENVIRONMENT 2019年 665卷 300-313页

作者： Zhu, Xiudi Zhang, Qiang Xu, Chong-Yu Sun, Peng Hu, Pan Beijing Normal Univ Key Lab Environm Change & Nat Disaster Minist Educ Beijing 100875 Peoples R China Beijing Normal Univ Acad Disaster Reduct & Emergency Management Fac Geog Sci Beijing 100875 Peoples R China Beijing Normal Univ State Key Lab Earth Surface Proc & Resources Ecol Beijing 100875 Peoples R China Univ Oslo Dept Geosci & Hydrol N-0316 Oslo Norway Anhui Normal Univ Coll Terr Resource & Tourism Wuhu 241002 Anhui Peoples R China

Good knowledge of the surface air temperature (SAT) is critical for scientific understanding of ecological environment changes and land-atmosphere thermodynamic interactions. However, sparse and uneven spatial distribution of the temperature gauging stations introduces remarkable uncertainties into analysis of the SAT pattern. From a geo-intelligent perspective, here we proposed a new SAT reconstruction method based on the multi-source data and machine learning technique which was developed by considering autocorrelation of the in situ observed SAT in both space and time, or simply STAML, i.e. Geoi-SVM (Geo-Intelligent Support Vector machine), Geoi-BPNN (Geo-Intelligent Back Propagation Neural Network) and Geoi-RF (Geo-Intelligent Random Forest). The multisource data used in this study include the in situ observed SAT and multisource remotely sensed data such as MODIS land surface temperature, NDVI (Normalized Difference Vegetation Index) data. Intermodel comparisons amidst reconstructed SAT data were done to evaluate reconstructing performance of abovementioned models. Besides, the SAT reconstructed by CART (Classification and Regression Tree) was also included to evaluate the reconstructing performance of the models considered in this study when compared to SAT data by CART algorithm. We found that the estimation error of the reconstructed SAT by the STAML is smaller than 0.5 K (Kelvin). In addition, it is interesting to note that the Geoi-RF performs better with Mean Absolute Error (MAE) of lower than 0.25 K, and Root Mean Squared Error (RMSE) and Standard Deviation (SD) of lower than 0.5 K respectively. Correlation coefficients between the reconstructed SAT by Geoi-RF and the observed SAT are close to 1. Besides, the estimation accuracy of the SAT by the Geoi-RF technique is 18.51-63.17% higher than that by the other techniques considered in this study. This study provides a new idea and technique for reconstruction of SAT over large spatial extent at regional a

关键词： Surface air temperature Multisource data Spatiotemporal autocorrelation Spatial resolution machine learning algorithm

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Forecasting 2030 CO2 reduction targets for Russia as a major emitter using different estimation scenarios

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JOURNAL OF APPLIED ECONOMICS 2023年第1期26卷

作者： Gurbanov, Sarvar Mikayilov, Jeyhun I. Mukhtarov, Shahriyar Yagubov, Sakit ADA Univ Sch Publ & Int Affairs SPIA Baku Azerbaijan King Abdullah Petr Studies & Res Ctr Energy & Macro & Microecon Riyadh Saudi Arabia Azerbaijan State Univ Econ UNEC Dept Stat & Econometr Baku Azerbaijan Vistula Univ Fac Econ & Int Relat Warsaw Poland Azerbaijan State Univ Econ UNEC UNEC Empir Res Ctr Baku Azerbaijan

This study firstly analyzes the impact of energy intensities and income on CO2 emissions in Russia, applying different estimation methods to the data period from 1990 to 2020. In addition, the study forecasts CO2 emissions considering 2030 targets under different assumptions and assesses the achievability of the set target. The estimation results concluded that the GDP and fossil fuel intensities of GDP have a statistically positive impact on CO2 emissions. Also, we found that the forecasted value for 2030, for the business-as-usual case, is 1750 MtCO2, with 95% confidence interval values of 1703 MtCO2 and 1796 MtCO2. This result shows that Russia needs to undergo substantial policy interventions to achieve its targets to reduce CO2 emissions. As the fifth biggest emitter, Russia missing its emissions targets will have undesirable implications for the rest of the world. Based on the projection results, the paper discusses some potential policy interventions.

关键词： CO2 emissions sustainable development machine learning algorithm forecasting Russia

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Rapid land use prediction via portable X-ray fluorescence (pXRF) data on the dried lakebed of Avlan Lake in Turkey

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GEODERMA REGIONAL 2022年 28卷

作者： Gozukara, Gafur Eskisehir Osmangazi Univ Dept Soil Sci & Plant Nutr TR-26160 Eskisehir Turkey

The rapid, non-destructive, and low-cost characterization of soil by using portable X-ray fluorescence (pXRF) has been increasingly attractive among soil scientists for soil pedogenesis research. Therefore, pXRF can aid to characterize by measuring elemental concentration, weathering indices, soil spectra for agricultural and lakebed areas. This study aimed to characterize the agricultural area (AA) and lakebed area (LA) through elemental concentration, weathering indices, and soil spectra based on pXRF spectral behavior and to evaluate the prediction and characterization performance of decision tree models for AA and LA. In total 108 soil samples (36 locations) were collected from three fixed depth intervals such as 0-20, 20-40, and 40-60 cm in both AA and LA in Elmali, Antalya, Turkey. All soil samples measured using pXRF to obtain elemental concentrations and soil spectra and results of input variables were randomly separated into 70% for calibration and 30% for validation. Savitzky-Golay Filter was applied for spectral data preprocessing. The area under the ROC curve (AUC) and validation accuracy were used to compare the prediction performance of seven different models. The results showed that the AA had the highest Mg, Si, Ca, Ti, P, Mn, Zn, Sr, and Zr concentrations compared with LA. The prediction performance of AUC (0.79) and validation accuracy (0.77) for elemental concentrations were acceptable, but other models were less satisfactory compared to the best model. In particular, P, Sr, and Fe were robust indicators to separate AA and LA using the decision tree model. These results suggested that the possibility of using pXRF for soil characterization and high prediction performance of AA and LA. This study highlights that elemental concentrations, weathering indices, and soil spectra based on pXRF measurements can be used as a useful proximal sensor to predict, characterize, separate, and investigate an understanding of soil characterization in AA and LA.

关键词： Entisols and inceptisols Avlan Lake Soil spectra Proximal sensor Decision tree Soil spatial variability machine learning algorithm

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Discrimination between Carbapenem-Resistant and Carbapenem-Sensitive Klebsiella pneumoniae Strains through Computational Analysis of Surface-Enhanced Raman Spectra: a Pilot Study

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MICROBIOLOGY SPECTRUM 2022年第1期10卷 e0240921页

作者： Liu, Wei Tang, Jia-Wei Lyu, Jing-Wen Wang, Jun-Jiao Pan, Ya-Cheng Shi, Xin-Yi Liu, Qing-Hua Zhang, Xiao Gu, Bing Wang, Liang Xuzhou Med Univ Sch Med Informat & Engn Dept Bioinformat Xuzhou Jiangsu Peoples R China Xuzhou Med Univ Med Technol Sch Xuzhou Jiangsu Peoples R China Xuzhou Med Univ Sch Life Sci Xuzhou Jiangsu Peoples R China Macau Univ Sci & Technol State Key Lab Qual Res Chinese Med Taipa Macao Peoples R China Guangdong Acad Med Sci Guangdong Prov Peoples Hosp Lab Med Guangzhou Peoples R China

In clinical settings, rapid and accurate diagnosis of antibiotic resistance is essential for the efficient treatment of bacterial infections. Conventional methods for antibiotic resistance testing are time consuming, while molecular methods such as PCR-based testing might not accurately reflect phenotypic resistance. Thus, fast and accurate methods for the analysis of bacterial antibiotic resistance are in high demand for clinical applications. In this pilot study, we isolated 7 carbapenem-sensitive Klebsiella pneumoniae (CSKP) strains and 8 carbapenem-resistant Klebsiella pneumoniae (CRKP) strains from clinical samples. Surface-enhanced Raman spectroscopy (SERS) as a label-free and noninvasive method was employed for discriminating CSKP strains from CRKP strains through computational analysis. Eight supervised machine learning algorithms were applied for sample analysis. According to the results, all supervised machine learning methods could successfully predict carbapenem sensitivity and resistance in IC pneumoniae, with a convolutional neural network (CNN) algorithm on top of all other methods. Taken together, this pilot study confirmed the application potentials of surface-enhanced Raman spectroscopy in fast and accurate discrimination of Klebsiella pneumoniae strains with different antibiotic resistance profiles. IMPORTANCE With the low-cost, label-free, and nondestructive features, Raman spectroscopy is becoming an attractive technique with great potential to discriminate bacterial infections. In this pilot study, we analyzed surfaced-enhanced Raman spectroscopy (SERS) spectra via supervised machine learning algorithms, through which we confirmed the application potentials of the SERS technique in rapid and accurate discrimination of Klebsiella pneumoniae strains with different antibiotic resistance profiles.

关键词： Klebsiella pneumoniae carbapenems surface-enhanced Raman spectroscopy machine learning algorithm antibiotic resistance profile

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Ensemble machine learning models for estimating mechanical curves of concrete-timber-filled steel tubes

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Engineering Applications of Artificial Intelligence 2025年 156卷

作者： Kazemi, F. Asgarkhani, N. Ghanbari-Ghazijahani, T. Jankowski, R. Faculty of Civil and Environmental Engineering Gdańsk University of Technology ul. Narutowicza 11/12 Gdansk 80-233 Poland School of Civil Engineering National Technical University of Athens 9 Iroon Polytechniou Str Zografou 15772 Greece School of Engineering Macquarie University Sydney Australia

The use of concrete-timber-filled steel tubes (CTFSTs) as composite structural elements in buildings is gaining attraction among researchers due to their positive structural behavior and high load-bearing capacity. The combination of steel, concrete and timber materials improves energy absorption and ductility making CTFSTs a promising choice for modern construction. However, finding the mechanical properties of CTFSTs is a challenge during the design process due to the complexity of predicting behavior. Reliable modeling of these interactions is essential for an optimal design, which requires extensive experimental data and advanced computational methodologies. Therefore, this study proposed ensemble machine learning (ML) models for estimating load-displacement and stress-strain curves as well as the maximum axial capacity and elastic stiffness of CTFSTs. The results confirm the reliability of ensemble ML models for predicting the elastic stiffness and the maximum axial capacity of CTFST specimens with error percentages of 0.57 and 0.72, respectively. In addition, proposed ensemble ML models were used to estimate axial load-displacement and stress-strain curves of CTFSTs having different shapes of timber, which their curve fitting ability were superior compared to other ML models (i.e., accuracy of 97.6 %). Having ensemble ML models validated by experimental tests, a graphical user interface (GUI) tool is prepared for the preliminary evaluation of CTFST specimens, which can ease the way for reducing the experimental costs. © 2025 The Authors

关键词： Concrete-timber-filled steel tubes Curve fitting-based model machine learning algorithm Mechanical curves Mechanical properties of composite elements

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machine learning positioning algorithms for long semi-monolithic scintillator PET detectors

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PHYSICS IN MEDICINE AND BIOLOGY 2025年第11期70卷 115018-115018页

作者： Kinyanjui, Samuel Mungai Kuang, Zhonghua Liu, Zheng Ren, Ning Yang, Yongfeng Chinese Acad Sci Shenzhen Inst Adv Technol Paul C Lauterbur Res Ctr Biomed Imaging Shenzhen 518055 Peoples R China Xiangnan Univ Sch Phys & Elect Elect Engn Microelect & Optoelect Technol Key Lab Hunan Highe Chenzhou 423000 Peoples R China Univ Chinese Acad Sci Shenzhen Coll Adv Technol Shenzhen 518055 Peoples R China Chinese Acad Sci Key Lab Biomed Imaging Sci & Syst Shenzhen 518055 Peoples R China

Objective. In this work, machine learning positioning algorithms are developed to improve the spatial resolutions of the semi-monolithic scintillator detectors in both monolithic (y) and depth of interaction (z) directions. Approach. Two long semi-monolithic scintillator detectors consisting of 12 lutetium yttrium oxyorthosilicate (LYSO) slabs of 0.96 x 56 x 10 mm3 and 14 LYSO slabs of 0.81 x 56 x 10 mm3 were manufactured. The scintillator arrays were read out by a 4 x 16 silicon photomultiplier array. 27 x 5 (y, z) positions of each detector were irradiated via a collimated 22Na pencil beam. Extreme gradient boosting (XGBoost) machine learning model was used to predict the interaction positions for y and z. The genetic algorithm (GA) or particle swarm optimization (PSO) algorithm was used to optimize hyperparameters for the XGBoost model. The results of the machine learning positioning algorithms were compared to analytical positioning methods. Main results. The GA and PSO algorithms provided similar results. Compared to the analytical methods, the machine learning positioning methods improved both y and z spatial resolutions especially at both ends of the detectors. The average y spatial resolutions using the machine learning positioning methods were 0.92 +/- 0.41 mm and 0.94 +/- 0.44 mm as compared to those obtained with the squared center of gravity method of 1.38 +/- 0.23 mm and 1.39 +/- 0.25 mm for the two detectors, respectively. The average z spatial resolutions obtained with the machine learning positioning methods were 1.67 +/- 0.41 mm and 1.68 +/- 0.45 mm as compared to those obtained with inverse standard deviation method of 2.09 +/- 0.82 mm and 2.14 +/- 0.81 mm for the two detectors, respectively. Significance. With the machine learning positioning algorithms, the semi-monolithic scintillator detectors with submillimeter slab thickness evaluated in this work provide less than 1 mm y spatial resolution and less than 2 mm z spatial resolution.

关键词： positron emission tomography (PET) PET detector semi-monolithic crystal machine learning algorithm extreme gradient boosting (XGBoost) genetic algorithm (GA) particle swarm optimization (PSO)

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machine learning-based prediction of CO2 solubility in methyldiethanolamine solutions: A comparative study

RESULTS IN ENGINEERING

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RESULTS IN ENGINEERING 2025年 26卷

作者： Fazeli, Sajjad Moradkhani, Mohammad Amin Bayati, Behrouz Ilam Univ Chem Engn Dept POB 69315-516 Ilam Iran

Methyldiethanolamine (MDEA) is a widely used solvent in carbon capture processes owing to its high absorption capacity. However, there is a lack of comprehensive predictive tools for estimating CO2 solubility in MDEA-based solution. To fulfil this research gap, in the current study, 2969 experimental data pertinent to the CO2 dissolution in MDEA solutions blended with various co-solvent, including water, amines, ionic liquids, electrolytes, etc., have been collected from the literature. The foregoing databank envelop a widespread range of pressures and temperatures. In order to construct robust models, three heuristic soft computing methods, including radial basis function neural network (RBF-NN), gaussian process regression (GPR) and multilayer perceptron neural network (MLP-NN) were employed. Despite the satisfactory performance of all intelligent models, the one designed based on the GPR method gave the superior accuracy with average absolute relative error (AARE) and R2 values of 4.94 % and 97.5 %, respectively, for the testing dataset. Moreover, it estimated more than 91 % of the analyzed samples within +/- 15 error margin. A statistical investigation through the William's plot implied the fact that both the collected databank and the suggested predictive tools benefit from high credibility. The novel models also favorably described the absorption capacity of diverse MDEA-based solutions under a wide range of operating conditions. Finally, the order of significance of influential factors in controlling solubility was determined based on a sensitivity analysis.

关键词： CO 2 capture Methyldiethanolamine (MDEA) machine learning algorithm Solubility Prediction

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Design of new truncated derivatives based on direct and reverse mirror repeats of first six residues of Caerin 4 antimicrobial peptide and evaluation of their activity and cytotoxicity

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CHEMICAL BIOLOGY & DRUG DESIGN 2020年第2期96卷 801-811页

作者： Madanchi, Hamid Sardari, Soroush Shajiee, Hooman Taherian, Sina Ashkar, Maryam Johari, Behrooz Shabani, Ali Akbar Sharafi, Shahram Semnan Univ Med Sci Sch Med Dept Biotechnol Semnan Iran Pasteur Inst Iran Biotechnol Res Ctr Dept Med Biotechnol Drug Design & Bioinformat Unit Tehran Iran Islamic Azad Univ Dept Biol Damghan Branch Damghan Iran Zanjan Univ Med Sci Sch Med Dept Med Biotechnol Zanjan Iran

Caerin 4 is a family of AMPs isolated from the frog called Litoria caerulea. In silico drug designing methods and using machine learning algorithms for AMPs design can reduce their usage restrictions such as production costs and the time required for investigation of their activity and toxicity. In this study, two short peptides were designed based on direct and reverse mirror repeats of GLWQKI conserved sequence from Caerin 4 family that called dCar12 and rCar12. Also, Caerin 4.1 was synthesized without primary GLWQKI sequence and named Car(7-23). Following the synthesis of peptides, their antimicrobial properties, cytotoxicity, secondary structure, and mode of action were further evaluated. Results indicated that rCar12 had a good antibacterial activity (at an MIC of 3.9-62.5 mu g/ml), while Car(7-23) did not have any antimicrobial properties. Cytotoxicity of rCar12 at MICs range was <5%, which is much less than Caerin 4.1. In conclusion, rCar12 with reverse mirror repeat has different functional properties compared with dCar12. These results corroborate the fact that in two peptides with identical residues and length, the position and arrangement of amino acids are very important concerning peptide function. Moreover, GLWQKI sequence is highly crucial for the antimicrobial activity of Caerin 4 antimicrobial peptide family.

关键词： antimicrobial peptide caerin in silico drug design Litoria caerulea machine learning algorithm

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Detection of power grid disturbances and cyber-attacks based on machine learning

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JOURNAL OF INFORMATION SECURITY AND APPLICATIONS 2019年 46卷 42-52页

作者： Wang, Defu Wang, Xiaojuan Zhang, Yong Jin, Lei Beijing Univ Posts & Telecommun Beijing Peoples R China

Modern intelligent power grid provides an efficient way of managing energy supply and consumption while facing numerous security threats at the same time. Both natural and man-made events can cause power system disturbance. Therefore, it is important for operators to identify the specific causes and types of disturbance in the power system to make decisions and respond appropriately. In order to address this problem, this paper proposes an attack detection model for power system based on machine learning that can be trained by using information and logs collected by phasor measurement units (PMUs). We carry out feature construction engineering, and then send the data to different machine learning models, in which random forest is chosen as the basic classifier of AdaBoost. The model is evaluated using open-source simulated power system data, which consists of 37 power system event scenarios. Finally, we compare the proposed model with other models by using different evaluation metrics. As the experimental results demonstrate that this model can achieve accuracy rate of 93.91% and detection rate of 93.6%, higher than eight recently developed techniques. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： machine learning algorithm Network attack Feature construction engineering Data processing

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Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools

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BRIEFINGS IN BIOINFORMATICS 2020年第2期21卷 408-420页

作者： Su, Ran Hu, Jie Zou, Quan Manavalan, Balachandran Wei, Leyi Ajou Univ Dept Physiol Sch Med Suwon South Korea Tianjin Univ Coll Intelligence & Comp Sch Comp Software Tianjin Peoples R China Tianjin Univ Tianjin Peoples R China Tianjin Univ Coll Intelligence & Comp Sch Comp Sci & Technol Tianjin Peoples R China Univ Elect Sci & Technol China Chengdu Sichuan Peoples R China Korea Inst Adv Study KIAS Seoul South Korea

Cell-penetrating peptides (CPPs) facilitate the delivery of therapeutically relevant molecules, including DNA, proteins and oligonucleotides, into cells both in vitro and in vivo. This unique ability explores the possibility of CPPs as therapeutic delivery and its potential applications in clinical therapy. Over the last few decades, a number of machine learning (ML)-based prediction tools have been developed, and some of them are freely available as web portals. However, the predictions produced by various tools are difficult to quantify and compare. In particular, there is no systematic comparison of the web-based prediction tools in performance, especially in practical applications. In this work, we provide a comprehensive review on the biological importance of CPPs, CPP database and existing ML-based methods for CPP prediction. To evaluate current prediction tools, we conducted a comparative study and analyzed a total of 12 models from 6 publicly available CPP prediction tools on 2 benchmark validation sets of CPPs and non-CPPs. Our benchmarking results demonstrated that a model from the KELM-CPPpred, namely KELM-hybrid-AAC, showed a significant improvement in overall performance, when compared to the other 11 prediction models. Moreover, through a length-dependency analysis, we find that existing prediction tools tend to more accurately predict CPPs and non-CPPs with the length of 20-25 residues long than peptides in other length ranges.

关键词： cell-penetrating peptides machine learning algorithm feature representation web servers

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