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Journal articles on the topic 'Hot spot prediction'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Zhang, Sijia, Le Zhao, Chun-Hou Zheng, and Junfeng Xia. "A feature-based approach to predict hot spots in protein–DNA binding interfaces." Briefings in Bioinformatics 21, no. 3 (April 8, 2019): 1038–46. http://dx.doi.org/10.1093/bib/bbz037.

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Abstract DNA-binding hot spot residues of proteins are dominant and fundamental interface residues that contribute most of the binding free energy of protein–DNA interfaces. As experimental methods for identifying hot spots are expensive and time consuming, computational approaches are urgently required in predicting hot spots on a large scale. In this work, we systematically assessed a wide variety of 114 features from a combination of the protein sequence, structure, network and solvent accessible information and their combinations along with various feature selection strategies for hot spot

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Kenneth Morrow, John, and Shuxing Zhang. "Computational Prediction of Protein Hot Spot Residues." Current Drug Metabolism 18, no. 9 (March 1, 2012): 1255–65. http://dx.doi.org/10.2174/138920012799362909.

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Kenneth Morrow, John, and Shuxing Zhang. "Computational Prediction of Protein Hot Spot Residues." Current Pharmaceutical Design 18, no. 9 (March 1, 2012): 1255–65. http://dx.doi.org/10.2174/138161212799436412.

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Nair B.J, Bipin, and Lijo Joy. "A hybrid approach for hot spot prediction and deep representation of hematological protein – drug interactions." International Journal of Engineering & Technology 7, no. 1.9 (March 1, 2018): 145. http://dx.doi.org/10.14419/ijet.v7i1.9.9752.

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In our research work we will collect the data of drugs as well as protein regarding hematic diseases, then applying feature extraction as well as classification, predict hot spot and non-hot spot then we are predicting the hot region using prediction algorithm. Parallelly from the hematological drug we are extracting the feature using molecular finger print then classifying using a classifier and applying deep learning concept to reduce the dimensionality then finally using machine learning algorithm predicting which drug will interact with the help of a hybrid approach.

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Tuncbag, N., O. Keskin, and A. Gursoy. "HotPoint: hot spot prediction server for protein interfaces." Nucleic Acids Research 38, Web Server (May 5, 2010): W402—W406. http://dx.doi.org/10.1093/nar/gkq323.

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Liu, Siyu, Chuyao Liu, and Lei Deng. "Machine Learning Approaches for Protein–Protein Interaction Hot Spot Prediction: Progress and Comparative Assessment." Molecules 23, no. 10 (October 4, 2018): 2535. http://dx.doi.org/10.3390/molecules23102535.

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Hot spots are the subset of interface residues that account for most of the binding free energy, and they play essential roles in the stability of protein binding. Effectively identifying which specific interface residues of protein–protein complexes form the hot spots is critical for understanding the principles of protein interactions, and it has broad application prospects in protein design and drug development. Experimental methods like alanine scanning mutagenesis are labor-intensive and time-consuming. At present, the experimentally measured hot spots are very limited. Hence, the use of

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Roll, Uri, Lewi Stone, and Shai Meiri. "Hot-Spot Facts and Artifacts-Questioning Israel's Great Biodiversity." Israel Journal of Ecology and Evolution 55, no. 3 (May 6, 2009): 263–79. http://dx.doi.org/10.1560/ijee.55.3.263.

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Israel's biological diversity has been praised as being particularly rich in relation to its size; however this assumption was never tested when taking into account the empirical form of the species-area relationship. Here we compared the species richness of different countries to see if the Israeli diversity is exceptionally rich when area is accurately accounted for. We compared richness of amphibians, birds, mammals, reptiles, flowering plants, conifers and cycads, and ferns in all the world's countries. We further tested the effects of mean latitude, altitude span, and insularity on specie

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Wang, Ao, and Yimin Xuan. "Multiscale prediction of localized hot-spot phenomena in solar cells." Renewable Energy 146 (February 2020): 1292–300. http://dx.doi.org/10.1016/j.renene.2019.07.073.

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Grosdidier, Solene, and Juan Fernandez-Recio. "Protein-protein Docking and Hot-spot Prediction for Drug Discovery." Current Pharmaceutical Design 18, no. 30 (August 23, 2012): 4607–18. http://dx.doi.org/10.2174/138161212802651599.

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Zhang, Ming, and Wei Chen. "Hot Spot Data Prediction Model Based on Wavelet Neural Network." Mathematical Problems in Engineering 2018 (October 30, 2018): 1–10. http://dx.doi.org/10.1155/2018/3719564.

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The novel hybrid multilevel storage system will be popular with SSD being integrated into traditional storage systems. To improve the performance of data migration between solid-state hard disk and hard disk according to the characteristics of each storage device, identifying the hot data block is significant issue. The hot data block prediction model based on wavelet neural network is built and trained by using historical data. This prediction model can overcome the cumulative effect of traditional statistical methods and has strong sensitivity to I/O loads with random variations. The experim

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Rommel, D. P., D. Di Maio, and T. Tinga. "Transformer hot spot temperature prediction based on basic operator information." International Journal of Electrical Power & Energy Systems 124 (January 2021): 106340. http://dx.doi.org/10.1016/j.ijepes.2020.106340.

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Skillen, Alex, Alistair Revell, Hector Iacovides, and Wei Wu. "Numerical prediction of local hot-spot phenomena in transformer windings." Applied Thermal Engineering 36 (April 2012): 96–105. http://dx.doi.org/10.1016/j.applthermaleng.2011.11.054.

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13

Zhang, Yue, Lianfei Shan, Jianming Yu та Hongwei Lv. "Transformer winding hot spot temperature prediction based on ε -fuzzy tree". IOP Conference Series: Earth and Environmental Science 300 (9 серпня 2019): 042034. http://dx.doi.org/10.1088/1755-1315/300/4/042034.

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Díaz-Valle, Armando, José Marcos Falcón-González, and Mauricio Carrillo-Tripp. "Hot Spots and Their Contribution to the Self-Assembly of the Viral Capsid: In Silico Prediction and Analysis." International Journal of Molecular Sciences 20, no. 23 (November 27, 2019): 5966. http://dx.doi.org/10.3390/ijms20235966.

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The viral capsid is a macromolecular complex formed by a defined number of self-assembled proteins, which, in many cases, are biopolymers with an identical amino acid sequence. Specific protein–protein interactions (PPI) drive the capsid self-assembly process, leading to several distinct protein interfaces. Following the PPI hot spot hypothesis, we present a conservation-based methodology to identify those interface residues hypothesized to be crucial elements on the self-assembly and thermodynamic stability of the capsid. We validate the predictions through a rigorous physical framework which

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Jin, Jae Sik, and Joon Sik Lee. "Electron–Phonon Interaction Model and Prediction of Thermal Energy Transport in SOI Transistor." Journal of Nanoscience and Nanotechnology 7, no. 11 (November 1, 2007): 4094–100. http://dx.doi.org/10.1166/jnn.2007.010.

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An electron–phonon interaction model is proposed and applied to thermal transport in semiconductors at micro/nanoscales. The high electron energy induced by the electric field in a transistor is transferred to the phonon system through electron–phonon interaction in the high field region of the transistor. Due to this fact, a hot spot occurs, which is much smaller than the phonon mean free path in the Si-layer. The full phonon dispersion model based on the Boltzmann transport equation (BTE) with the relaxation time approximation is applied for the interactions among different phonon branches a

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Jin, Jae Sik, and Joon Sik Lee. "Electron–Phonon Interaction Model and Prediction of Thermal Energy Transport in SOI Transistor." Journal of Nanoscience and Nanotechnology 7, no. 11 (November 1, 2007): 4094–100. http://dx.doi.org/10.1166/jnn.2007.18084.

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An electron–phonon interaction model is proposed and applied to thermal transport in semiconductors at micro/nanoscales. The high electron energy induced by the electric field in a transistor is transferred to the phonon system through electron–phonon interaction in the high field region of the transistor. Due to this fact, a hot spot occurs, which is much smaller than the phonon mean free path in the Si-layer. The full phonon dispersion model based on the Boltzmann transport equation (BTE) with the relaxation time approximation is applied for the interactions among different phonon branches a

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Higa, Roberto Hiroshi, and Clésio Luis Tozzi. "Prediction of binding hot spot residues by using structural and evolutionary parameters." Genetics and Molecular Biology 32, no. 3 (2009): 626–33. http://dx.doi.org/10.1590/s1415-47572009000300029.

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18

Deng, Lei, Yuanchao Sui, and Jingpu Zhang. "XGBPRH: Prediction of Binding Hot Spots at Protein–RNA Interfaces Utilizing Extreme Gradient Boosting." Genes 10, no. 3 (March 21, 2019): 242. http://dx.doi.org/10.3390/genes10030242.

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Hot spot residues at protein–RNA complexes are vitally important for investigating the underlying molecular recognition mechanism. Accurately identifying protein–RNA binding hot spots is critical for drug designing and protein engineering. Although some progress has been made by utilizing various available features and a series of machine learning approaches, these methods are still in the infant stage. In this paper, we present a new computational method named XGBPRH, which is based on an eXtreme Gradient Boosting (XGBoost) algorithm and can effectively predict hot spot residues in protein–RN

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Chen, Peng, Jinyan Li, Limsoon Wong, Hiroyuki Kuwahara, Jianhua Z. Huang, and Xin Gao. "Accurate prediction of hot spot residues through physicochemical characteristics of amino acid sequences." Proteins: Structure, Function, and Bioinformatics 81, no. 8 (July 23, 2013): 1351–62. http://dx.doi.org/10.1002/prot.24278.

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Shao, Yong-Bo, Zhi-Fu Du, and Seng-Tjhen Lie. "Prediction of hot spot stress distribution for tubular K-joints under basic loadings." Journal of Constructional Steel Research 65, no. 10-11 (October 2009): 2011–26. http://dx.doi.org/10.1016/j.jcsr.2009.05.004.

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Chen, Zixi, Fuqiang Liu, Bin Li, Xiaoqing Peng, Lin Fan, and Aijing Luo. "Prediction of hot spot areas of hemorrhagic fever with renal syndrome in Hunan Province based on an information quantity model and logistical regression model." PLOS Neglected Tropical Diseases 14, no. 12 (December 21, 2020): e0008939. http://dx.doi.org/10.1371/journal.pntd.0008939.

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Background China’s “13th 5-Year Plan” (2016–2020) for the prevention and control of sudden acute infectious diseases emphasizes that epidemic monitoring and epidemic focus surveys in key areas are crucial for strengthening national epidemic prevention and building control capacity. Establishing an epidemic hot spot areas and prediction model is an effective means of accurate epidemic monitoring and surveying. Objective: This study predicted hemorrhagic fever with renal syndrome (HFRS) epidemic hot spot areas, based on multi-source environmental variable factors. We calculated the contribution

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22

Deng, Yongqing, Jiangjun Ruan, Yu Quan, Ruohan Gong, Daochun Huang, Cihan Duan, and Yiming Xie. "A Method for Hot Spot Temperature Prediction of a 10 kV Oil-Immersed Transformer." IEEE Access 7 (2019): 107380–88. http://dx.doi.org/10.1109/access.2019.2924709.

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Mohamadi, Bahaa, Timo Balz, and Ali Younes. "Towards a PS-InSAR Based Prediction Model for Building Collapse: Spatiotemporal Patterns of Vertical Surface Motion in Collapsed Building Areas—Case Study of Alexandria, Egypt." Remote Sensing 12, no. 20 (October 12, 2020): 3307. http://dx.doi.org/10.3390/rs12203307.

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Buildings are vulnerable to collapse incidents. We adopt a workflow to detect unusual vertical surface motions before building collapses based on PS-InSAR time series analysis and spatiotemporal data mining techniques. Sentinel-1 ascending and descending data are integrated to decompose vertical deformation in the city of Alexandria, Egypt. Collapsed building data were collected from official sources, and overlayed on PS-InSAR vertical deformation results. Time series deformation residuals are used to create a space–time cube in the ArcGIS software environment and analyzed by emerging hot spot

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24

Kim, Jeong Guk, Byeong Choon Goo, Sung Cheol Yoon, and Sung Tae Kwon. "Thermographic Investigation of Hot Spots in Railway Brake Discs." Key Engineering Materials 385-387 (July 2008): 669–72. http://dx.doi.org/10.4028/www.scientific.net/kem.385-387.669.

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Damage evolution due to generation of hot spots on railway brake disc was investigated using the infrared thermography method. A brake disc with gray cast iron, which is currently used in Korea, was employed for this investigation. A high-speed infrared camera was used to measure the surface temperature of brake disc as well as for in-situ monitoring of hot spot evolution. From the thermographic images, the observed hot spots and thermal damage of railway brake disc during braking operation were qualitatively analyzed. Moreover, in this investigation, the previous experimental and theoretical

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Zhao, Yueyao, Jiawei Zhang, and Haojie Li. "Deformation prediction analysis of vertical displacement of deep foundation pit based on LIBSVM." E3S Web of Conferences 206 (2020): 01021. http://dx.doi.org/10.1051/e3sconf/202020601021.

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The reliable prediction of the surface vertical displacement deformation of deep foundation pits is of great significance to the excavation of large foundation pits. The support vector machine model (LIBSVM) has become a hot spot in the prediction of deep foundation pit deformation and provides a new prediction for the deformation of deep foundation pits. In this paper, taking the deep foundation pit of Daoxianghu Road Station in xx as an example, a prediction model of vertical displacement on the ground is established based on LIBSVM and analysis shows that the prediction results based on the

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Xu, Yan, Kai Zhang, Hong Liang Zheng, Yu Cheng Sun, and Xue Lei Tian. "An Improved Geometric Model to Predict Hot Spots of Castings." Materials Science Forum 689 (June 2011): 29–32. http://dx.doi.org/10.4028/www.scientific.net/msf.689.29.

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It is very important to predict the hot spots of castings properly, which is known as a criterion for riser design. In this paper, an improved geometric model for hot spot prediction is proposed, and subsequently, its application to hot spot analysis is presented. As we know, the heat dissipation potential of a location in a casting depends on its distance to the heat transfer surfaces. In a meshed casting, the reciprocal of distance from a certain cell to surfaces is calculated at all the six orthogonal directions, by which the heat dissipation potentials of every cell will be evaluated consi

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Zhang, Yiyi, Xingxiao Wei, Xianhao Fan, Ke Wang, Ran Zhuo, Wei Zhang, Shuo Liang, Jian Hao, and Jiefeng Liu. "A Prediction Model of Hot Spot Temperature for Split-Windings Traction Transformer Considering the Load Characteristics." IEEE Access 9 (2021): 22605–15. http://dx.doi.org/10.1109/access.2021.3056529.

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Shabarek, Abdullah, Steven Chien, and Soubhi Hadri. "Deep Learning Framework for Freeway Speed Prediction in Adverse Weather." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 10 (August 27, 2020): 28–41. http://dx.doi.org/10.1177/0361198120947421.

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The introduction of deep learning (DL) models and data analysis may significantly elevate the performance of traffic speed prediction. Adverse weather causes mobility and safety concerns because of varying traffic speeds with poor visibility and road conditions. Most previous modeling approaches have not considered the heterogeneity of temporal and spatial data, such as traffic and weather conditions. This paper presents a framework, consisting of two DL models, to predict traffic speed under normal conditions and during adverse weather, considering prevailing traffic speed, wind speed, traffi

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Xia, Linyuan, Qiumei Huang, and Dongjin Wu. "Decision Tree-Based Contextual Location Prediction from Mobile Device Logs." Mobile Information Systems 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/1852861.

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Contextual location prediction is an important topic in the field of personalized location recommendation in LBS (location-based services). With the advancement of mobile positioning techniques and various sensors embedded in smartphones, it is convenient to obtain massive human mobile trajectories and to derive a large amount of valuable information from geospatial big data. Extracting and recognizing personally interesting places and predicting next semantic location become a research hot spot in LBS. In this paper, we proposed an approach to predict next personally semantic place with histo

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Pruncu, C. I., Z. Azari, C. Casavola, and C. Pappalettere. "Characterization and Prediction of Cracks in Coated Materials: Direction and Length of Crack Propagation in Bimaterials." International Scholarly Research Notices 2015 (January 31, 2015): 1–13. http://dx.doi.org/10.1155/2015/594147.

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The behaviour of materials is governed by the surrounding environment. The contact area between the material and the surrounding environment is the likely spot where different forms of degradation, particularly rust, may be generated. A rust prevention treatment, like bluing, inhibitors, humidity control, coatings, and galvanization, will be necessary. The galvanization process aims to protect the surface of the material by depositing a layer of metallic zinc by either hot-dip galvanizing or electroplating. In the hot-dip galvanizing process, a metallic bond between steel and metallic zinc is

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Freitas e Silva, Kleber Santiago, Raisa Melo Lima, Patrícia de Sousa Lima, Lilian Cristiane Baeza, Roosevelt Alves da Silva, Célia Maria de Almeida Soares, and Maristela Pereira. "Interaction of Isocitrate Lyase with Proteins Involved in the Energetic Metabolism in Paracoccidioides lutzii." Journal of Fungi 6, no. 4 (November 23, 2020): 309. http://dx.doi.org/10.3390/jof6040309.

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Background: Systemic mycosis is a cause of death of immunocompromised subjects. The treatment directed to evade fungal pathogens shows severe limitations, such as time of drug exposure and side effects. The paracoccidioidomycosis (PCM) treatment depends on the severity of the infection and may last from months to years. Methods: To analyze the main interactions of Paracoccidioides lutzii isocitrate lyase (ICL) regarding the energetic metabolism through affinity chromatography, we performed blue native PAGE and co-immunoprecipitation to identify ICL interactions. We also performed in silico ana

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Matijosaitiene, Irina, Peng Zhao, Sylvain Jaume, and Joseph Gilkey Jr. "Prediction of Hourly Effect of Land Use on Crime." ISPRS International Journal of Geo-Information 8, no. 1 (December 31, 2018): 16. http://dx.doi.org/10.3390/ijgi8010016.

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Predicting the exact urban places where crime is most likely to occur is one of the greatest interests for Police Departments. Therefore, the goal of the research presented in this paper is to identify specific urban areas where a crime could happen in Manhattan, NY for every hour of a day. The outputs from this research are the following: (i) predicted land uses that generates the top three most committed crimes in Manhattan, by using machine learning (random forest and logistic regression), (ii) identifying the exact hours when most of the assaults are committed, together with hot spots duri

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Kim, Sung-Min, Yosoon Choi, and Hyeong-Dong Park. "New Outlier Top-Cut Method for Mineral Resource Estimation via 3D Hot Spot Analysis of Borehole Data." Minerals 8, no. 8 (August 11, 2018): 348. http://dx.doi.org/10.3390/min8080348.

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Three-dimensional (3D) analysis of borehole data is very important for effective mineral exploration. It can be used not only to understand the geological structure of the underground, but to estimate the amount of the resource. In the mining industry, the geostatistical interpolation, such as kriging, is widely used to predict the value of a whole section using this borehole data. In order to obtain reasonable prediction results, it is firstly necessary to verify assay and geological databases. In addition, if the assayed grade data deviates significantly from the average value, it is necessa

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Kunicki, Borucki, Cichoń, and Frymus. "Modeling of the Winding Hot-Spot Temperature in Power Transformers: Case Study of the Low-Loaded Fleet." Energies 12, no. 18 (September 17, 2019): 3561. http://dx.doi.org/10.3390/en12183561.

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A proposal of the dynamic thermal rating (DTR) applied and optimized for low-loaded power transformers equipped with on-line hot-spot (HS) measuring systems is presented in the paper. The proposed method concerns the particular population of mid-voltage (MV) to high-voltage (HV) transformers, a case study of the population of over 1500 units with low average load is analyzed. Three representative real-life working units are selected for the method evaluation and verification. Temperatures used for analysis were measured continuously within two years with 1 h steps. Data from 2016 are used to t

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Guan, Mingxiang, Le Wang, and Liming Chen. "Channel allocation for hot spot areas in HAPS communication based on the prediction of mobile user characteristics." Intelligent Automation & Soft Computing 22, no. 4 (April 7, 2016): 613–20. http://dx.doi.org/10.1080/10798587.2016.1152771.

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Zhu, Xiaolei, and Julie C. Mitchell. "KFC2: A knowledge-based hot spot prediction method based on interface solvation, atomic density, and plasticity features." Proteins: Structure, Function, and Bioinformatics 79, no. 9 (July 6, 2011): 2671–83. http://dx.doi.org/10.1002/prot.23094.

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Araújo, J. A., L. Susmel, D. Taylor, J. C. T. Ferro, and J. L. A. Ferreira. "On the prediction of high-cycle fretting fatigue strength: Theory of critical distances vs. hot-spot approach." Engineering Fracture Mechanics 75, no. 7 (May 2008): 1763–78. http://dx.doi.org/10.1016/j.engfracmech.2007.03.026.

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Sun, Yuanyuan, Gongde Xu, Na Li, Kejun Li, Yongliang Liang, Hui Zhong, Lina Zhang, and Ping Liu. "Hotspot Temperature Prediction of Dry-Type Transformers Based on Particle Filter Optimization with Support Vector Regression." Symmetry 13, no. 8 (July 22, 2021): 1320. http://dx.doi.org/10.3390/sym13081320.

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Both poor cooling methods and complex heat dissipation lead to prominent asymmetry in transformer temperature distribution. Both the operating life and load capacity of a power transformer are closely related to the winding hotspot temperature. Realizing accurate prediction of the hotspot temperature of transformer windings is the key to effectively preventing thermal faults in transformers, thus ensuring the reliable operation of transformers and accurately predicting transformer operating lifetimes. In this paper, a hot spot temperature prediction method is proposed based on the transformer

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SHI, GUANGLIN, LIN ZHU, and DONGBIN WEI. "A NEW PREDICTION APPROACH FOR THE STRUCTURAL FATIGUE LIFE BASED ON MULTI-FACTOR CORRECTION." Surface Review and Letters 25, no. 05 (July 2018): 1850095. http://dx.doi.org/10.1142/s0218625x18500956.

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As the phenomenon of fatigue damage is a common failure mode of equipment, the reliability evaluation and life prediction have become a hot-spot. The precise prediction of fatigue life in the initiation stage has become necessary. The common prediction study for structural fatigue life takes less influence factors into consideration. The common prediction results of fatigue life cannot be quantitatively corrected by the influence factors at the same time. This paper presents a research on the prediction approach for structural fatigue life based on the multi-factor correction. The influence of

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Mo, Shu Min, Ke Feng Zeng, and Chao Liu. "Early Warning Mechanism of Huangshan World Geopark to Divert Passenger Traffic." Advanced Materials Research 1030-1032 (September 2014): 2014–18. http://dx.doi.org/10.4028/www.scientific.net/amr.1030-1032.2014.

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Aiming at some hot spots scenic traffic overload during the golden week due to downward travel quality, mainly from the perspective of economic geography, focus on researching and building the early warning system of Huangshan World Geopark tourism mechanism. To get the environmental capacity, calculate tourism environment capacity, analysis and prediction of passenger traffic, scenic spot warning signs of Huangshan World Geopark.Attempts to construct a system, from the view of early warning signal and scattering tourist traffic, discusses the problem of tourism warning system.Put forward the

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SRINIVASAN, M., and A. KRISHNAN. "ASSESSING THE RELIABILITY OF TRANSFORMER TOP OIL TEMPERATURE MODEL." International Journal of Reliability, Quality and Safety Engineering 19, no. 05 (October 2012): 1250024. http://dx.doi.org/10.1142/s0218539312500246.

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The hot spot temperature (HST) plays a most important role in the insulation life of the transformer. Ambient temperature and environmental variable factors involved in the top oil temperature (TOT) computations in all transformer thermal models affects insulation lifetime either directly or indirectly. The importance of the ambient temperature in transformer's insulation life, a new semi-physically-based model for the estimation of TOT in transformers has been proposed in this paper. The winding hot-spot temperature can be calculated as function of the TOT that can be estimated by using the a

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Ruzicka, Filip, and Tim Connallon. "Is the X chromosome a hot spot for sexually antagonistic polymorphisms? Biases in current empirical tests of classical theory." Proceedings of the Royal Society B: Biological Sciences 287, no. 1937 (October 21, 2020): 20201869. http://dx.doi.org/10.1098/rspb.2020.1869.

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Females and males carry nearly identical genomes, which can constrain the evolution of sexual dimorphism and generate conditions that are favourable for maintaining sexually antagonistic (SA) polymorphisms, in which alleles beneficial for one sex are deleterious for the other. An influential theoretical prediction, by Rice (Rice 1984 Evolution 38 , 735–742), is that the X chromosome should be a ‘hot spot’ (i.e. enriched) for SA polymorphisms. While important caveats to Rice's theoretical prediction have since been highlighted (e.g. by Fry (2010) Evolution 64 , 1510–1516), several empirical stu

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Matijosaitiene, Irina, Anthony McDowald, and Vishal Juneja. "Predicting Safe Parking Spaces: A Machine Learning Approach to Geospatial Urban and Crime Data." Sustainability 11, no. 10 (May 19, 2019): 2848. http://dx.doi.org/10.3390/su11102848.

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This research aims to identify spatial and time patterns of theft in Manhattan, NY, to reveal urban factors that contribute to thefts from motor vehicles and to build a prediction model for thefts. Methods include time series and hot spot analysis, linear regression, elastic-net, Support vector machines SVM with radial and linear kernels, decision tree, bagged CART, random forest, and stochastic gradient boosting. Machine learning methods reveal that linear models perform better on our data (linear regression, elastic-net), specifying that a higher number of subway entrances, graffiti, and res

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Lu, Jian Hui, Meng Bing Wei, and Kai Yuan Zheng. "Multiaxial Fatigue Life Prediction of the CII Platform Leg Based on Critical Plane Energy Method." Applied Mechanics and Materials 624 (August 2014): 255–61. http://dx.doi.org/10.4028/www.scientific.net/amm.624.255.

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Based on the critical plane energy method to build a plastic strain energy function on the critical plane, the approach of hot spot plastic strain energy as assessment parameters of fatigue damage is used and the shortcomings that the traditional energy method as a scalar is difficult to describe the direction of crack propagation is overcomed. By the rules of cracks expansion through critical plane, the fatigue life model parameters have a clear physical significance. W-S algorithm process is deduced, so the complex stress state is equivalent to a series of symmetric cyclic stress based on en

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Feuerstein, Stefanie, and Kerstin Schepanski. "Identification of Dust Sources in a Saharan Dust Hot-Spot and Their Implementation in a Dust-Emission Model." Remote Sensing 11, no. 1 (December 20, 2018): 4. http://dx.doi.org/10.3390/rs11010004.

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Although mineral dust plays a key role in the Earth’s climate system and in climate and weather prediction, models still have difficulties in predicting the amount and distribution of mineral dust in the atmosphere. One reason for this is the limited understanding of the distribution of dust sources and their behavior with respect to their spatiotemporal variability in activity. For a better estimation of the atmospheric dust load, this paper presents an approach to localize dust sources and thereby estimate the sediment supply for a study area centered on the Aïr Massif in Niger with a north–

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Orozco, G. A., J. R. Gomez, O. F. Sanchez, I. D. Gil, and A. Duran. "Effect of kinetic models on hot spot temperature prediction for phthalic anhydride production in a multitubular packed bed reactor." Canadian Journal of Chemical Engineering 88, no. 2 (April 2010): 224–31. http://dx.doi.org/10.1002/cjce.20276.

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Schlee, Sandra, Kristina Straub, Thomas Schwab, Thomas Kinateder, Rainer Merkl, and Reinhard Sterner. "Prediction of quaternary structure by analysis of hot spot residues in protein‐protein interfaces: the case of anthranilate phosphoribosyltransferases." Proteins: Structure, Function, and Bioinformatics 87, no. 10 (June 10, 2019): 815–25. http://dx.doi.org/10.1002/prot.25744.

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Chen, Ya Bo, Yue Sun, Xu Ri Sun, Ge Hao Sheng, and Xiu Chen Jiang. "Real-Time Temperature On-Line Monitoring and Analysis System for Transformers." Applied Mechanics and Materials 521 (February 2014): 409–13. http://dx.doi.org/10.4028/www.scientific.net/amm.521.409.

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The safe operation of power transformers mainly depends on proper functioning of insulation, whose status is revealed by temperatures. Applying ZigBee wireless network, a real-time temperature on-line monitoring and analysis system is developed to view the operation status of underground distribution transformers and process fault diagnosis. Furthermore, using the top-oil and hot-spot temperature calculation method in IEEE Std C57.91-1995, the system can compute a prediction of those temperatures with current load ratio and ambient temperature. System will display early warnings if temperature

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Ding, Guangyu, and Liangxi Qin. "Study on the prediction of stock price based on the associated network model of LSTM." International Journal of Machine Learning and Cybernetics 11, no. 6 (November 30, 2019): 1307–17. http://dx.doi.org/10.1007/s13042-019-01041-1.

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AbstractStock market has received widespread attention from investors. It has always been a hot spot for investors and investment companies to grasp the change regularity of the stock market and predict its trend. Currently, there are many methods for stock price prediction. The prediction methods can be roughly divided into two categories: statistical methods and artificial intelligence methods. Statistical methods include logistic regression model, ARCH model, etc. Artificial intelligence methods include multi-layer perceptron, convolutional neural network, naive Bayes network, back propagat

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Shi, H. L., and G. W. Lan. "A GREY MODEL FOR SHORT-TERM PREDICTION OF THE IONOSPHERIC TEC." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 8, 2020): 1161–67. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-1161-2020.

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Abstract. Accurate prediction of TEC can significantly improve the accuracy of navigation and positioning, therefore TEC observation and prediction has become a hot spot in ionospheric research. TEC has the characteristics of nonlinearity and non-stationarity, that cannot accurately describe this change by analytic expressions. Through the analysis of TEC content changes at the same time for several consecutive days in different seasons, it can be concluded that the TEC change at the same time in a short period is relatively stable, the overall monotonous change trend has a certain correlation

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