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Dynamic changes of the ecological footprint in the Beijing-Tianjin-Hebei region from 1996 to 2020

ECOLOGICAL INDICATORS 2020年 112卷 106142-000页

作者： Liu, Lingna Lei, Yalin China Univ Geosci Sch Humanities & Econ Management Beijing Peoples R China Minist Land & Resources Key Lab Carrying Capac Assessment Resource & Envi Beijing Peoples R China

The ecological footprint (EF) describes the complex relationship between the eco-environment and economic development. EF dynamics can better reflect the appropriation of natural resources in various countries or regions compared with previous studies. This paper identified six dominant factors including population, per capita GDP, three major industrial added values, and energy consumption using grey correlation model for the EF changes of the Beijing-Tianjin-Hebei (Jing-Jin-Ji) region from 1996 to 2015. Then, we predicted the EF of the Jing-Jin-Ji region from 2016 to 2020 based on support vector machine model. (1) Since 1996, the EF and per capita EF of the overall Jing-Jin-Ji region have increased, of which those of Hebei Province and Tianjin increased while Beijing decreased. (2) Energy consumption dominated the EF of Jing-Jin-Ji region. The population of Beijing had a high correlation coefficient of 0.735 with the local EF, and the degree of correlation between the EF and per capita GDP was 0.812 in Hebei Province. In Tianjin, the added value of the tertiary industry was closely correlated with its EF, with a correlation coefficient of 0.741. (3) The EF of the Jing-Jin-Ji region will reach 778.30 million hectares by 2020, an increase of 10% compared with the value in 2015. Finally, suggestions for the development of industrial structure and energy consumption are listed for the sustainable development of Jing-Jin-Ji region.

关键词： Ecological footprint Grey correlational model support vector machine model Energy consumption Jing-Jin-Ji region

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Assessing the druggability of protein-protein interactions by a supervised machine-learning method

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BMC BIOINFORMATICS 2009年第1期10卷 1-13页

作者： Sugaya, Nobuyoshi Ikeda, Kazuyoshi PharmaDesign Inc Div Res & Dev Drug Discovery Dept Chuo Ku Tokyo 1040032 Japan

Background: Protein-protein interactions (PPIs) are challenging but attractive targets of small molecule drugs for therapeutic interventions of human diseases. In this era of rapid accumulation of PPI data, there is great need for a methodology that can efficiently select drug target PPIs by holistically assessing the druggability of PPIs. To address this need, we propose here a novel approach based on a supervised machine-learning method, support vector machine (SVM). Results: To assess the druggability of the PPIs, 69 attributes were selected to cover a wide range of structural, drug and chemical, and functional information on the PPIs. These attributes were used as feature vectors in the SVM-based method. Thirty PPIs known to be druggable were carefully selected from previous studies;these were used as positive instances. Our approach was applied to 1,295 human PPIs with tertiary structures of their protein complexes already solved. The best SVM model constructed discriminated the already-known target PPIs from others at an accuracy of 81% (sensitivity, 82%;specificity, 79%) in cross-validation. Among the attributes, the two with the greatest discriminative power in the best SVM model were the number of interacting proteins and the number of pathways. Conclusion: Using the model, we predicted several promising candidates for druggable PPIs, such as SMAD4/SKI. As more PPI data are accumulated in the near future, our method will have increased ability to accelerate the discovery of druggable PPIs.

关键词： Tertiary Structure Test Instance support vector machine model Radial Basis Function Kernel Positive Instance

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An improved sequence based prediction protocol for DNA-binding proteins using SVM and comprehensive feature analysis

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BMC BIOINFORMATICS 2013年第1期14卷 1-14页

作者： Zou, Chuanxin Gong, Jiayu Li, Honglin E China Univ Sci & Technol Shanghai Key Lab New Drug Design State Key Lab Bioreactor Engn Sch Pharm Shanghai 200237 Peoples R China

Background: DNA-binding proteins (DNA-BPs) play a pivotal role in both eukaryotic and prokaryotic proteomes. There have been several computational methods proposed in the literature to deal with the DNA-BPs, many informative features and properties were used and proved to have significant impact on this problem. However the ultimate goal of Bioinformatics is to be able to predict the DNA-BPs directly from primary sequence. Results: In this work, the focus is how to transform these informative features into uniform numeric representation appropriately and improve the prediction accuracy of our SVM-based classifier for DNA-BPs. A systematic representation of some selected features known to perform well is investigated here. Firstly, four kinds of protein properties are obtained and used to describe the protein sequence. Secondly, three different feature transformation methods (OCTD, AC and SAA) are adopted to obtain numeric feature vectors from three main levels: Global, Nonlocal and Local of protein sequence and their performances are exhaustively investigated. At last, the mRMR-IFS feature selection method and ensemble learning approach are utilized to determine the best prediction model. Besides, the optimal features selected by mRMR-IFS are illustrated based on the observed results which may provide useful insights for revealing the mechanisms of protein-DNA interactions. For five-fold cross-validation over the DNAdset and DNAaset, we obtained an overall accuracy of 0.940 and 0.811, MCC of 0.881 and 0.614 respectively. Conclusions: The good results suggest that it can efficiently develop an entirely sequence-based protocol that transforms and integrates informative features from different scales used by SVM to predict DNA-BPs accurately. Moreover, a novel systematic framework for sequence descriptor-based protein function prediction is proposed here.

关键词： support vector machine support vector machine model Ensemble Learning Protein Function Prediction Dipeptide Composition

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GIS-based landslide susceptibility mapping in the Longmen Mountain area (China) using three different machine learning algorithms and their comparison

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第38期30卷 88612-88626页

作者： Huang, Ziyan Peng, Li Li, Sainan Liu, Ying Zhou, Shuang Sichuan Normal Univ Coll Geog & Resources Chengdu 610101 Peoples R China Sichuan Normal Univ Key Lab Land Resources Evaluat & Monitoring Southw Minist Educ Chengdu 610101 Peoples R China Chinese Acad Sci Inst Mt Hazards & Environm Chengdu 610041 Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources Res Beijing 100101 Peoples R China

Landslides are a common natural disaster, having severe socio-economic effects and posing immense threat to safety, such as loss of life at a global scale. modeling and predicting the possibility of landslides are important in order to monitor and prevent their negative consequences. In this study, landslides are the primary research object. Further, the frequency ratio (FR) method was applied to the random forest (RF), support vector machine (SVM), and decision tree (DT) regression algorithms for landslide sensitivity assessment. It was also applied to landslide risk assessment mapping in the Longmen Mountain area. Therefore, taking into account the positive and negative sample balance, 7774 historical landslide points and 7774 non-landslide points were selected and divided them into training sets and test sets. The influence factors were selected and analyzed through multicollinearity analysis and the FR method. To improve the performance of the model and the accuracy of the findings, the individual environmental factors are normalized. Subsequently, the LSI (landslide susceptibility index), was obtained by calculating the frequency ratio. Following this, the RF, SVM, and DT were used to construct the model. The trained model calculates the landslide probability of each cell in the study area and generates the resultant susceptibility map. The receiver operating characteristic (ROC) curve and R-2 of this region were calculated to evaluate the model's performance. The results indicate that RF obtained the highest predictive performance (area under the curve (AUC) = 0.82) in landslide risk prediction, followed by SVM (AUC = 0.8) and DT (AUC = 0.69). The results of this study serve as a predictive map for landslide susceptibility areas and provide critical support for the security of lives and property for the human and socio-economic development in the Longmen Mountain region. In addition, the experiment results reveal that the machine learning model based on the FR

关键词： Landslide susceptibility assessment Random forest model Frequency ratio method support vector machine model Longmen Mountain area

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Prediction of FAD interacting residues in a protein from its primary sequence using evolutionary information

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BMC BIOINFORMATICS 2010年第Sup1期11卷 S48-S48页

作者： Mishra, Nitish K. Raghava, Gajendra P. S. Inst Microbial Technol Sect 39A Chandigarh India

Background: Flavin binding proteins (FBP) plays a critical role in several biological functions such as electron transport system (ETS). These flavoproteins contain very tightly bound, sometimes covalently, flavin adenine dinucleotide (FAD) or flavin mono nucleotide (FMN). The interaction between flavin nucleotide and amino acids of flavoprotein is essential for their functionality. Thus identification of FAD interacting residues in a FBP is an important step for understanding their function and mechanism. Results: In this study, we describe models developed for predicting FAD interacting residues using 15, 17 and 19 window pattern. support vector machine (SVM) based models have been developed using binary pattern of amino acid sequence of protein and achieved maximum accuracy 69.65% with Mathew's Correlation Coefficient (MCC) 0.39 and Area Under Curve (AUC) 0.773. The performance of these models have been improved significantly from 69.65% to 82.86% with MCC 0.66 and AUC 0.904, when evolutionary information is used as input in SVM. The evolutionary information was generated in form of position specific score matrix (PSSM) profile by using PSI-BLAST at e-value 0.001. All models were developed on 198 non-redundant FAD binding protein chains containing 5172 FAD interacting residues and evaluated using fivefold cross-validation technique. Conclusion: This study suggests that evolutionary information of 17 amino acid patterns perform best for FAD interacting residues prediction. We also developed a web server which predicts FAD interacting residues in a protein which is freely available for academics.

关键词： support vector machine Area Under Curve support vector machine model Flavin Adenine Dinucleotide Evolutionary Information

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Application of Hyperspectral Imaging to Identify Pine Seed Varieties

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JOURNAL OF APPLIED SPECTROSCOPY 2023年第4期90卷 916-923页

作者： Ma, Jianing Pang, Lei Guo, Yuemeng Wang, Jinghua Ma, Jingjing He, Fang Yan, Lei Beijing Forestry Univ Sch Technol Beijing Peoples R China

Seed variety purity is the main indicator of seed quality, which affects crop yield and product quality. In the present study, a new method for the identification of pine nut varieties based on hyperspectral imaging and convolutional neural networks LeNet-5 was established so as to avoid the hybridization of different varieties of pine nuts, improve the identification efficiency and reduce the cost of identification. Images of 128 wavelengths in the 370-1042 nm range were acquired by hyperspectral imaging. The spectrum and image of each seed were obtained by means of black-and-white correction and region segmentation of the original image. Twenty characteristic wavelengths were extracted from the first three principal components (PCs) of principal component analysis (PCA). A support vector machine (SVM) spectral recognition model based on full wavelengths and characteristic wavelengths was established. For different species of pine seeds, the classification accuracies of the prediction set in the aforementioned datasets were 97.7 and 93.1%, respectively. The seed images of 20 characteristic wavelengths were input into LeNet-5 to improve the network structure and the number of convolution channels. The improved LeNet-5 performed better with over 99% accuracy. Such results show that the convolutional neural network is of considerable significance for fast and nondestructive identification of pine seed varieties.

关键词： pine seed variety identification hyperspectral imaging support vector machine model principal component analysis convolutional neural networks

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Drought prediction models driven by meteorological and remote sensing data in Guanzhong Area, China

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HYDROLOGY RESEARCH 2020年第5期51卷 942-958页

作者： Li, Jianzhu Zhang, Siyao Huang, Lingmei Zhang, Ting Feng, Ping Tianjin Univ State Key Lab Hydraul Engn Simulat & Safety Tianjin Peoples R China Xian Univ Technol Fac Water Resources & Hydroelect Engn Xian Peoples R China

Drought is an important factor that limits economic and social development due to its frequent occurrence and profound influence. Therefore, it is of great significance to make accurate predictions of drought for early warning and disaster alleviation. In this paper, SPEI-1 was confirmed to classify drought grades in the Guanzhong Area, and the autoregressive integrated moving average (ARIMA), random forest (RF) and support vector machine (SVM) model were established. Meteorological data and remote sensing data were used to derive the prediction models. The results showed the following. (1) The SVM model performed the best when the models were developed using meteorological data, remote sensing data and a combination of meteorological and remote sensing data, but the model's corresponding kernel functions are different and include linear, polynomial and Gaussian radial basis kernel functions, respectively. (2) The RF model driven by the remote sensing data and the SVM model driven by the combined meteorological and remote sensing data were found to perform better than the model driven by the corresponding other data in the Guanzhong Area. It is difficult to accurately measure drought with the single meteorological data. Only by considering the combined factors can we more accurately monitor and predict drought. This study can provide an important scientific basis for regional drought warnings and predictions.

关键词： drought prediction integrated autoregressive moving average model random forest model standardized precipitation evaporation index support vector machine model

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A co-optimization method of thermal-stress coupling 3D integrated system with through silicon via

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2023年第12期66卷 1-13页

作者： Wang, Xianglong Chen, Dongdong Li, Di Yang, Yintang Xidian Univ Sch Microelect Xian 710071 Peoples R China

In this paper, a co-optimization design method for thermal-stress coupling 3-dimensional integrated system with through silicon via is proposed based on the finite element method, support vector machine model and modified particle swarm optimization algorithm. In the cause of analyzing the effects of geometrical parameters (radius of through silicon via, oxide thickness and the height of oxide insolation layer) on the thermal-stress distribution, the finite element method based COMSOL software is conducted to simulate the thermal-stress coupling 3-dimensional integrated system. Based on the simulation data obtained by COMSOL, the support vector machine models are adopted to establish the database for describing the relationships between the geometrical parameters and key indexes (peak temperature, peak stress and temperature difference) to improve the design efficiency. Based on the desired key indexes of thermal-stress coupling 3-dimensional integrated system, the multi-objective evaluation function is formulated. Then, the geometrical parameters are optimized by the modified particle swarm optimization algorithm. The finite element simulation is conducted to verify the effectiveness of the proposed strategy. In three cases, the errors between the simulated and desired temperature differences are all less than 0.28 K, and the relative errors between the simulated and desired peak temperature/peak stress are all less than 4.78%, which indicates that the geometrical parameters can be optimized to control the key indexes of thermal-stress coupling 3-dimensional integrated system. Therefore, the developed method can be used in the design and manufacture of 3-dimensional integrated system.

关键词： Co-optimization support vector machine model Thermal-stress coupling Through silicon via 3-dimensional integrated system

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Using support vector machine models for Size of Motorcycle Taxi Terminal in Bang Yai District Analysis

Using Support Vector Machine Models for Size of Motorcycle T...

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作者： Morakot Worachairungreung Kunyaphat Thanakunwutthirot Thammasak Songka Faculty of Humanities and Social Sciences Suan Sunandha Rajabhat University Fuculty of Industrail Technology Suan Sunandha Rajabhat University Academic Service Center Burapha University

This study aims to study the factors of building usage that influence the size of the motorcycle taxi ***,as there are many factors of building usage,this research aims to study whether the modern non-parametric model,such as,the support vector machine(SVM),is able to classify the size of the motorcycle taxi terminal,using the dataset of the building *** on random 50 grid cells,with the size of 500 *** ratio of the correct classification of the training data is 85 percents,whereas the SVM model is able to predict the size *** ratio of the correct classification of the testing data is 100 *** though,one of the SVM model's limitations is the Black Box *** Black Box prevents the model from classifying the factors that directly influence the motorcycle taxi terminal's ***,the author uses that dataset for the correlation test,in order to find the correlation *** this case,the factors that are highly correlated with the motorcycle taxi terminal's sizes are the number of ATMs and the bus stations.

关键词： support vector machine model Motorcycle taxi terminal Correlation Building usage

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Identification of NAD interacting residues in proteins

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BMC BIOINFORMATICS 2010年第1期11卷 1-8页

作者： Ansari, Hifzur R. Raghava, Gajendra P. S. Inst Microbial Technol Chandigarh 160036 India

Background: Small molecular cofactors or ligands play a crucial role in the proper functioning of cells. Accurate annotation of their target proteins and binding sites is required for the complete understanding of reaction mechanisms. Nicotinamide adenine dinucleotide (NAD(+) or NAD) is one of the most commonly used organic cofactors in living cells, which plays a critical role in cellular metabolism, storage and regulatory processes. In the past, several NAD binding proteins (NADBP) have been reported in the literature, which are responsible for a wide-range of activities in the cell. Attempts have been made to derive a rule for the binding of NAD(+) to its target proteins. However, so far an efficient model could not be derived due to the time consuming process of structure determination, and limitations of similarity based approaches. Thus a sequence and non-similarity based method is needed to characterize the NAD binding sites to help in the annotation. In this study attempts have been made to predict NAD binding proteins and their interacting residues (NIRs) from amino acid sequence using bioinformatics tools. Results: We extracted 1556 proteins chains from 555 NAD binding proteins whose structure is available in Protein Data Bank. Then we removed all redundant protein chains and finally obtained 195 non-redundant NAD binding protein chains, where no two chains have more than 40% sequence identity. In this study all models were developed and evaluated using five-fold cross validation technique on the above dataset of 195 NAD binding proteins. While certain type of residues are preferred (e.g. Gly, Tyr, Thr, His) in NAD interaction, residues like Ala, Glu, Leu, Lys are not preferred. A support vector machine (SVM) based method has been developed using various window lengths of amino acid sequence for predicting NAD interacting residues and obtained maximum Matthew's correlation coefficient (MCC) 0.47 with accuracy 74.13% at window length 17. We also developed a

关键词： support vector machine Protein Data Bank Aldose Reductase support vector machine model Nicotinamide Adenine Dinucleotide

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