Journal articles: 'Hot spot prediction'

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 prediction. We then trained and compared four commonly used machine learning models, namely, support vector machine (SVM), random forest, Naïve Bayes and k-nearest neighbor, for the identification of hot spots using 10-fold cross-validation and the independent test set. Our results show that (1) features based on the solvent accessible surface area have significant effect on hot spot prediction; (2) different but complementary features generally enhance the prediction performance; and (3) SVM outperforms other machine learning methods on both training and independent test sets. In an effort to improve predictive performance, we developed a feature-based method, namely, PrPDH (Prediction of Protein–DNA binding Hot spots), for the prediction of hot spots in protein–DNA binding interfaces using SVM based on the selected 10 optimal features. Comparative results on benchmark data sets indicate that our predictor is able to achieve generally better performance in predicting hot spots compared to the state-of-the-art predictors. A user-friendly web server for PrPDH is well established and is freely available at http://bioinfo.ahu.edu.cn:8080/PrPDH.

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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 computational approaches to predicting hot spots is becoming increasingly important. Here, we describe the basic concepts and recent advances of machine learning applications in inferring the protein–protein interaction hot spots, and assess the performance of widely used features, machine learning algorithms, and existing state-of-the-art approaches. We also discuss the challenges and future directions in the prediction of hot spots.

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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 species richness both for all world countries and just for Mediterranean countries. For all taxa and in all tests, Israel lies within the prediction intervals of the models. Out of 42 tests, Israel's residuals lie in the upper decile of positive residuals once: for reptiles, when compared to all world countries, taking all predicting factors into account. Using only countries larger than 1000 km2, Israel was placed as top residual when compared to other Mediterranean countries for mammals and reptiles. We therefore conclude that Israel's species richness does not significantly exceed the expected values for a country its size. This is true when comparing it to either world or just Mediterranean countries. Adding more predicting factors does not change this fact.

8

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 experimental results show that the proposed model has better accuracy and faster learning speed than BP neural network model. In addition, it has less dependence on sample data and has better generalization ability and robustness. This model can be applied to the data migration of distributed hybrid storage systems to improve performance.

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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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Zhang, Yue, Lianfei Shan, Jianming Yu, and Hongwei Lv. "Transformer winding hot spot temperature prediction based on ε -fuzzy tree." IOP Conference Series: Earth and Environmental Science 300 (August 9, 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 integrates molecular dynamics simulations and free energy calculations by Umbrella sampling and the potential of mean force using an all-atom molecular representation of the capsid proteins of an icosahedral virus in an explicit solvent. Our results show that a single mutation in any of the structure-conserved hot spots significantly perturbs the quaternary protein–protein interaction, decreasing the absolute value of the binding free energy, without altering the protein’s secondary nor tertiary structure. Our conservation-based hot spot prediction methodology can lead to strategies to rationally modulate the capsid’s thermodynamic properties.

15

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 and different phonon frequencies. The Joule heating by the electron–phonon scattering is modeled through the intervalley and intravalley processes for silicon by introducing average electron energy. The simulation results are compared with those obtained by the full phonon dispersion model which treats the electron–phonon scattering as a volumetric heat source. The comparison shows that the peak temperature in the hot spot region is considerably higher and more localized than the previous results. The thermal characteristics of each phonon mode are useful to explain the above phenomena. The optical mode phonons of negligible group velocity obtain the highest energy density from electrons, and resides in the hot spot region without any contribution to heat transport, which results in a higher temperature in that region. Since the acoustic phonons with low group velocity show the higher energy density after electron–phonon scattering, they induce more localized heating near the hot spot region. The ballistic features are strongly observed when phonon–phonon scattering rates are lower than 4 × 1010 s−1.

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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 and different phonon frequencies. The Joule heating by the electron–phonon scattering is modeled through the intervalley and intravalley processes for silicon by introducing average electron energy. The simulation results are compared with those obtained by the full phonon dispersion model which treats the electron–phonon scattering as a volumetric heat source. The comparison shows that the peak temperature in the hot spot region is considerably higher and more localized than the previous results. The thermal characteristics of each phonon mode are useful to explain the above phenomena. The optical mode phonons of negligible group velocity obtain the highest energy density from electrons, and resides in the hot spot region without any contribution to heat transport, which results in a higher temperature in that region. Since the acoustic phonons with low group velocity show the higher energy density after electron–phonon scattering, they induce more localized heating near the hot spot region. The ballistic features are strongly observed when phonon–phonon scattering rates are lower than 4 × 1010 s−1.

17

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–RNA interfaces utilizing an optimal set of properties. Firstly, we download 47 protein–RNA complexes and calculate a total of 156 sequence, structure, exposure, and network features. Next, we adopt a two-step feature selection algorithm to extract a combination of 6 optimal features from the combination of these 156 features. Compared with the state-of-the-art approaches, XGBPRH achieves better performances with an area under the ROC curve (AUC) score of 0.817 and an F1-score of 0.802 on the independent test set. Meanwhile, we also apply XGBPRH to two case studies. The results demonstrate that the method can effectively identify novel energy hotspots.

19

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 weight of each environmental factor to the morbidity risk, obtained the spatial probability distribution of HFRS risk areas within the study region, and detected and extracted epidemic hot spots, to guide accurate epidemic monitoring as well as prevention and control. Methods: We collected spatial HFRS data, as well as data on various types of natural and human social activity environments in Hunan Province from 2010 to 2014. Using the information quantity method and logistic regression modeling, we constructed a risk-area-prediction model reflecting the epidemic intensity and spatial distribution of HFRS. Results: The areas under the receiver operating characteristic curve of training samples and test samples were 0.840 and 0.816. From 2015 to 2019, HRFS case site verification showed that more than 82% of the cases occurred in high-risk areas. Discussion This research method could accurately predict HFRS hot spot areas and provided an evaluation model for Hunan Province. Therefore, this method could accurately detect HFRS epidemic high-risk areas, and effectively guide epidemic monitoring and surveyance.

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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 analysis to extract spatiotemporal patterns for vertical deformation around collapsed buildings. Our results show two spatiotemporal patterns of new cold spot or new hot spot before the incidents in 66 out of 68 collapsed buildings between May 2015 and December 2018. The method was validated in detail on four collapsed buildings between January and May 2019, proving the applicability of this workflow to create a temporal vulnerability map for building collapse monitoring. This study is a step forward to create a PS-InSAR based model for building collapse prediction in the city.

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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 studies on hot spots phenomenon were reviewed, and the current experimental results were introduced and compared with theoretical prediction.

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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 model are in good agreement with the measured data, and the MSE reaches 0.0323. The model is effective and has an effective prospective skill.

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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 considering the influences of the neighboring grids. With the improved geometric model, there is no iteration during calculation, and only twice of cell traverse is required. The first traverse gets the distance reciprocal and the second focuses on the heat dissipation potential. The result of this model, which turns out similar to that of procedures based on heat transfer equations, reflects solidification sequence in a casting, hence the hot spots will be known instantaneously. Obviously this geometric model ignores many conditions during solidification process. However, messages like locations of hot spots are shown much faster and more conveniently than that of procedures based on heat transfer equations. Therefore, it is believed that it will shorten much time for casting technology design.

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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, traffic volume, road capacity, wind bearing, precipitation intensity, and visibility. To ensure the accuracy of speed prediction, different DL models were assessed. The results indicated that the proposed one-dimensional convolutional neural network model outperformed others in relation to the least root mean square error and the least mean absolute error. Considering real-time weather data feeds on a 15-min basis, a tool was also developed for displaying predicted traffic speeds on New Jersey freeways. Application of the proposed framework models for predicting spatio-temporal hot-spot congestion caused by adverse weather is discussed.

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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 historical visiting patterns derived from mobile device logs. To address the problems of location imprecision and lack of semantic information, a modified trip-identify method is employed to extract key visit points from GPS trajectories to a more accurate extent while semantic information are added through stay point detection and semantic places recognition. At last, a decision tree model is adopted to explore the spatial, temporal, and sequential features in contextual location prediction. To validate the effectiveness of our approach, experiments were conducted based on a trajectory collection in Guangzhou downtown area. The results verified the feasibility of our approach on contextual location prediction from continuous mobile devices logs.

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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 obtained by immersing the steel in a zinc bath at a temperature of around 460°C. Although the hot-dip galvanizing procedure is recognized to be one of the most effective techniques to combat corrosion, cracks can arise in the intermetallic δ layer. These cracks can affect the life of the coated material and decrease the lifetime service of the entire structure. In the present paper the mechanical response of hot-dip galvanized steel submitted to mechanical loading condition is investigated. Experimental tests were performed and corroborative numerical and analytical methods were then applied in order to describe both the mechanical behaviour and the processes of crack/cracks propagation in a bimaterial as zinc-coated material.

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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 analysis by homology, docking, hot-spot prediction and contact preference analysis to identify the conformation of ICL complexes. Results: ICL interacted with 18 proteins in mycelium, 19 in mycelium-to-yeast transition, and 70 in yeast cells. Thirty complexes were predicted through docking and contact preference analysis. ICL has seven main regions of interaction with protein partners. Conclusions: ICL seems to interfere with energetic metabolism of P. lutzii, regulating aerobic and anaerobic metabolism as it interacts with proteins from glycolysis, gluconeogenesis, TCA and methylcitrate cycles, mainly through seven hot-spot residues.

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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 during these hours, by applying time series and hot spot analysis, (iii) built hourly prediction models for assaults based on the land use, by deploying logistic regression. Assault, as a physical attack on someone, according to criminal law, is identified as the third most committed crime in Manhattan. Land use (residential, commercial, recreational, mixed use etc.) is assigned to every area or lot in Manhattan, determining the actual use or activities within each particular lot. While plotting assaults on the map for every hour, this investigation has identified that the hot spots where assaults occur were ‘moving’ and not confined to specific lots within Manhattan. This raises a number of questions: Why are hot spots of assaults not static in an urban environment? What makes them ‘move’—is it a particular urban pattern? Is the ‘movement’ of hot spots related to human activities during the day and night? Answering these questions helps to build the initial frame for assault prediction within every hour of a day. Knowing a specific land use vulnerability to assault during each exact hour can assist the police departments to allocate forces during those hours in risky areas. For the analysis, the study is using two datasets: a crime dataset with geographical locations of crime, date and time, and a geographic dataset about land uses with land use codes for every lot, each obtained from open databases. The study joins two datasets based on the spatial location and classifies data into 24 classes, based on the time range when the assault occurred. Machine learning methods reveal the effect of land uses on larceny, harassment and assault, the three most committed crimes in Manhattan. Finally, logistic regression provides hourly prediction models and unveils the type of land use where assaults could occur during each hour for both day and night.

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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 necessary to perform the prediction including the outlier top-cut because it may excessively affect the predicted value. However, the existing top-cut methods of determining a specific threshold value may cause an error by excluding significant data. In this study, to minimize the loss of such data, we developed a 3D hot spot analysis technique to analyze statistically significant outliers. In addition, it was applied to borehole data analysis of the Au deposit. As a result, we confirmed that the proposed method can mitigate the overestimation or underestimation that might occur when applying the existing methods.

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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 train selected models based on various machine learning (ML) algorithms. Data from 2017 are used to verify the trained models and to validate the method. Accuracy analysis of all applied ML algorithms is discussed and compared to the conventional thermal model. As a result, the best accuracy of the prediction of HS temperatures is yielded by a generalized linear model (GLM) with mean prediction error below 0.71% for winding HS. The proposed method may be implemented as a part of the technical assessment decision support systems and freely adopted for other electrical power apparatus after relevant data are provided for the learning process and as predictors for trained models.

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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 operating parameters through the particle filter optimization support vector regression model. Based on the monitored transformer temperature, load rate, transformer cooling type, and ambient temperature, the hotspot temperature of a dry-type transformer can be predicted by a support vector regression method. The hyperparameters of the support vector regression are dynamically optimized here according to the particle filter to improve the optimization accuracy. The validity and accuracy of the proposed method are verified by comparing the proposed method with a traditional support vector regression method based on the real operating data of a 35 kV dry-type 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 some factors on the fatigue life was analyzed and the prediction approach for structural fatigue life based on multi-factor correction was raised. Then the fatigue life of axle housing was predicted by using the corrected approach, as the case study. Moreover, the result predicted by the local stress–strain approach and the experimental data were used to verify the accuracy of the corrected approach. It can be clearly demonstrated by the results that the corrected prediction approach can be used to achieve the precision fatigue life for engineering structures. Further, it is also a prediction approach endowed with engineering application prospects.

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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 measures to achieve reasonable control and diversion of passenger traffic for promoting tourism effect.

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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 ambient temperature, wind velocity and solar heat radiation effect and transformer loading measured data. The estimated TOT is compared with measured data of a distribution transformer in operation. The proposed model has been validated using real data gathered from a 100 MVA power transformer. For a semi-physically-based model to be acceptable, it must have the qualities of: adequacy, accuracy and consistency. We assess model adequacy using the scale: prediction R2, and plot of residuals against fitted values. To assess model consistency, we use: variance inflation factor (VIF) (which measure multicollinearity), condition number. To assess model accuracy we use mean square error, maximum and minimum error values of semi-physically-based model parameters to the existing model parameters.

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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 studies appear to support it. Here, we show that current tests of Rice's theory—most of which are based on quantitative genetic measures of fitness (co)variance—are frequently biased towards detecting X-linked effects. We show that X-linked genes tend to contribute disproportionately to quantitative genetic patterns of SA fitness variation whether or not the X is enriched for SA polymorphisms. Population genomic approaches for detecting SA loci, including genome-wide association study of fitness and analyses of intersexual F ST , are similarly biased towards detecting X-linked effects. In the light of our models, we critically re-evaluate empirical evidence for Rice's theory and discuss prospects for empirically testing it.

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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 restaurants on streets contribute to higher theft rates from motor vehicles. Although the prediction model for thefts meets almost all assumptions (five of six), its accuracy is 77%, suggesting that there are other undiscovered factors making a contribution to the generation of thefts. As an output demonstrating final results, the application prototype for searching safer parking in Manhattan, NY based on the prediction model, has been developed.

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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 energy and structural damage caused by everyone is calculated. The research, fatigue life prediction of the key component on CII platform under random waves and flow loadings, has theoretical significance and value of engineering application.

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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–south extent of 16 ∘ –22 ∘ N and an east–west extent of 4 ∘ –12 ∘ E. This approach uses optical Sentinel-2 data at visible and near infrared wavelengths together with HydroSHEDS flow accumulation data to localize ephemeral riverbeds. Visible channels from Sentinel-2 data are used to detect sand sheets and dunes. The identified sediment supply map was compared to the dust source activation frequency derived from the analysis of Desert-Dust-RGB imagery from the Meteosat Second Generation series of satellites. This comparison reveals the strong connection between dust activity, prevailing meteorology and sediment supply. In a second step, the sediment supply information was implemented in a dust-emission model. The simulated emission flux shows how much the model results benefit from the updated sediment supply information in localizing the main dust sources and in retrieving the seasonality of dust activity from these sources. The described approach to characterize dust sources can be implemented in other regional model studies, or even globally, and can thereby help to get a more accurate picture of dust source distribution and a more realistic estimation of the atmospheric dust load.

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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 temperatures are much higher than expected ones, in which way insulation aging can be handled in advance. Insulation fault and big disasters will be prevented.

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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 propagation network, single-layer LSTM, support vector machine, recurrent neural network, etc. But these studies predict only one single value. In order to predict multiple values in one model, it need to design a model which can handle multiple inputs and produces multiple associated output values at the same time. For this purpose, it is proposed an associated deep recurrent neural network model with multiple inputs and multiple outputs based on long short-term memory network. The associated network model can predict the opening price, the lowest price and the highest price of a stock simultaneously. The associated network model was compared with LSTM network model and deep recurrent neural network model. The experiments show that the accuracy of the associated model is superior to the other two models in predicting multiple values at the same time, and its prediction accuracy is over 95%.

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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. Since the grey model performs better in the prediction of a small amount of data and has a high accuracy in the prediction of time series with monotonous changes, it is used in the prediction of the same time and point-to-point short-term prediction of TEC. The accuracy of the grey model is verified by the Posterior variance ratio, the Little error probability test and the relation grade. The residual correction is made for the prediction results with low prediction accuracy, by further establishing the GM (1,1) model of residual values, and the original prediction results being compensated and refined by the residual GM (1,1) model. The experimental results show that the improved model is more accurate than the grey prediction model and can reflect the changing characteristics of ionospheric TEC.

Journal articles on the topic 'Hot spot prediction'

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