Density Prediction Models for Energetic Compounds Merely Using Molecular Topology

2021 ◽  
Author(s):  
Chunming Yang ◽  
Jie Chen ◽  
Runwen Wang ◽  
Miao Zhang ◽  
Chaoyang Zhang ◽  
...  
Keyword(s):  
Prediction Models ◽  
Molecular Topology ◽  
Energetic Compounds ◽  
Density Prediction

scholarly journals A Novel PM2.5 Concentrations Probability Density Prediction Model Combines The Least Absolute Shrinkage And Selection Operator With Quantile Regression

2021 ◽  
Author(s):  
Shaomei Yang ◽  
Haoyue Wu
Keyword(s):  
Quantile Regression ◽  
Probability Density ◽  
Prediction Models ◽  
Negative Impact ◽  
Influential Factors ◽  
Percentage Error ◽  
Lasso Regression ◽  
Density Prediction ◽  
Significant Negative Impact ◽  
Selection Operator

Abstract PM2.5 has a significant negative impact on human health and atmospheric quality, and accurate prediction of its concentration is necessary. PM2.5 concentration is influenced by a combination of factors from both meteorological conditions and air quality. It is essential to identify the significant factors influencing PM2.5 concentrations in the prediction process. To address this issue, this paper proposes the quantile regression (QR) model based on the least absolute shrinkage and selection operator (LASSO), combined with kernel density estimation (KDE) for probabilistic density prediction of PM2.5 concentrations. The model uses LASSO regression to select the influential factors, and then the quartiles of daily PM2.5 concentrations obtained using the QR model are imported into the KDE model to obtain the probability density curves of PM2.5 concentrations. In this paper, empirical analysis is performed with the data sets of Beijing, China, and Jinan, China, and the accuracy of the model is evaluated using the mean absolute percentage error(MAPE) and the relative mean square error (RMSE). The simulation results reveal that the LASSSO-QR-KDE model has a higher accuracy than the traditional prediction models and the currently used research models. The model provides a novel and excellent tool for policy makers to predict PM2.5 concentrations.

scholarly journals Study on optimization of rake head density of suction hopper dredger based on bat algorithm and extreme learning machine

2020 ◽  
Vol 309 ◽  
pp. 04018
Author(s):  
Guangjie Hao ◽  
Menghong Yu ◽  
Zhen Su
Keyword(s):  
Extreme Learning Machine ◽  
Prediction Models ◽  
Prediction Method ◽  
Bat Algorithm ◽  
Yangtze Estuary ◽  
Extreme Learning Machines ◽  
Learning Machines ◽  
Density Prediction ◽  
Learning Machine ◽  
Selection Of

The dredging output of suction dredger mainly comes from the suction density of the rake head. Accurate prediction of suction density is of great significance to improve the dredging output of suction dredger. In order to overcome the shortcomings of low accuracy and poor real-time performance of the current inhalation density prediction methods, a bat algorithm is proposed to optimize the inhalation density prediction method of extreme learning machine. The bat algorithms for optimizing extreme learning machines prediction model is constructed based on the measured construction data of “Xinhaifeng” Yangtze Estuary, and compared with other prediction models. Finally, the bat algorithms for optimizing extreme learning machines model is used to build the output simulator of inhalation density. Compared with the actual construction, the selection of control parameters is analyzed when the output of inhalation density is the best. Experients show that bat algorithms for optimizing extreme learning machines prediction has high accuracy and good stability, and can provide scientific and effective reference for yield prediction and construction guidance.

scholarly journals Pulpwood green density prediction models and sampling-based calibration

Silva Fennica ◽  
2021 ◽  
Vol 55 (4) ◽  
Author(s):  
Jaakko Repola ◽  
Juha Heikkinen ◽  
Jari Lindblad
Keyword(s):  
Prediction Models ◽  
Prediction Errors ◽  
Green Density ◽  
Predictive Regression ◽  
Density Prediction ◽  
Measured Weight ◽  
Model Predictions ◽  
Density Factor ◽  
Relative Root ◽  
Current Practices

Pulpwood arriving at the mills is mainly measured by weighing. In the loading phase of forwarding and trucking, timber is weighed using scales mounted in the grapple loader. The measured weight of timber is converted into volume using a conversion factor defined as green density (kg m). At the mill, the green density factor is determined by sampling measurements, while in connection with weighing with grapple-mounted scales during transportation, fixed green density factors are used. In this study, we developed predictive regression models for the green density of pulpwood. The models were constructed separately by pulpwood assortments: pine (contains mainly L); spruce (mainly (L.) Karst.); decayed spruce; birch (mainly Ehrh. and Roth); and aspen (mainly L.). Study material was composed of the sampling-based measurements at the mills between 2013–2019. The models were specified as linear mixed models with both fixed and random parameters. The fixed effect produced the expected value of green density as a function of delivery week, storage time, and meteorological conditions during storage. The random effects allowed the model calibration by utilizing the previous sampling weight measurements. The model validation showed that the model predictions faithfully reproduced the observed seasonal variation in green density. They were more reliable than those obtained with the current practices. Even the uncalibrated (fixed) predictions had lower relative root mean squared prediction errors than those obtained with the current practices.–3Pinus sylvestrisPicea abiesBetula pubescensBetula pendulaPopulus tremula

scholarly journals Corrections of Molecular Morphology and Hydrogen Bond for Improved Crystal Density Prediction

Molecules ◽  
2019 ◽  
Vol 25 (1) ◽  
pp. 161 ◽  
Author(s):  
Linyuan Wang ◽  
Miao Zhang ◽  
Jie Chen ◽  
Liang Su ◽  
Shicao Zhao ◽  
...  
Keyword(s):  
Hydrogen Bond ◽  
Energetic Materials ◽  
Prediction Models ◽  
Molecular Design ◽  
Small Data ◽  
Detonation Pressure ◽  
Data Set ◽  
Density Prediction ◽  
Crystal Density ◽  
Molecular Morphology

Density prediction is of great significance for molecular design of energetic materials, since detonation velocity linearly with density and detonation pressure increases with the density squared. However, the accuracy and generalization of former reported prediction models need further improvement, because most of them are derived from small data sets and few molecular descriptors. As shown in this paper, for molecules presenting brick-like shape or containing more hydrogen-bond donors the predicted densities have large negative deviations from experimental values. Thus, a molecular morphology descriptor η and a hydrogen-bond descriptor Hb are introduced as correction items to build 3 new QSPR models. Besides, 3694 nitro compounds are adopted as data set by this work. The accuracies are obviously improved, and the generalizations are verified by an independent test set. At the level of B3PW91/6-31G(d,p), the effective ratios (ERs) of the 3 Equations, for Δρ < 5%, are 92.7%, 91.8%, and 93.3%; for Δρ < 2%, the values are 53.5%, 51.3%, and 54.7%. At the level of B3LYP/6-31G**, for Δρ < 5%, the values are 92.3%, 91.4% and 92.9%; for Δρ < 2%, the values are 53.7%, 51.4% and 53.2%.

4 Pattern Recognition for Healing Burns to Compute Evidence: Space Syntax and Machine Learning Analysis of Burns Center Karachi

2013 ◽  
Vol 1 (1) ◽  
pp. 13
Author(s):  
Javaria Manzoor Shaikh ◽  
JaeSeung Park
Keyword(s):  
Pattern Recognition ◽  
Prediction Models ◽  
Uv Light ◽  
Healing Process ◽  
Space Syntax ◽  
Depth Map ◽  
Routine Tasks ◽  
Hyper Plane ◽  
First Time ◽  
Learning Analysis

Usually elongated hospitalization is experienced byBurn patients, and the precise forecast of the placement of patientaccording to the healing acceleration has significant consequenceon healthcare supply administration. Substantial amount ofevidence suggest that sun light is essential to burns healing andcould be exceptionally beneficial for burned patients andworkforce in healthcare building. Satisfactory UV sunlight isfundamental for a calculated amount of burn to heal; this delicaterather complex matrix is achieved by applying patternclassification for the first time on the space syntax map of the floorplan and Browder chart of the burned patient. On the basis of thedata determined from this specific healthcare learning technique,nurse can decide the location of the patient on the floor plan, hencepatient safety first is the priority in the routine tasks by staff inhealthcare settings. Whereas insufficient UV light and vitamin Dcan retard healing process, hence this experiment focuses onmachine learning design in which pattern recognition andtechnology supports patient safety as our primary goal. In thisexperiment we lowered the adverse events from 2012- 2013, andnearly missed errors and prevented medical deaths up to 50%lower, as compared to the data of 2005- 2012 before this techniquewas incorporated.In this research paper, three distinctive phases of clinicalsituations are considered—primarily: admission, secondly: acute,and tertiary: post-treatment according to the burn pattern andhealing rate—and be validated by capable AI- origin forecastingtechniques to hypothesis placement prediction models for eachclinical stage with varying percentage of burn i.e. superficialwound, partial thickness or full thickness deep burn. Conclusivelywe proved that the depth of burn is directly proportionate to thedepth of patient’s placement in terms of window distance. Ourfindings support the hypothesis that the windowed wall is mosthealing wall, here fundamental suggestion is support vectormachines: which is most advantageous hyper plane for linearlydivisible patterns for the burns depth as well as the depth map isused.

Bankruptcy Prediction Models: A Bet on Artificial Neural Networks

2012 ◽  
Vol 3 (2) ◽  
pp. 48-50
Author(s):  
Ana Isabel Velasco Fernández ◽  
◽  
Ricardo José Rejas Muslera ◽  
Juan Padilla Fernández-Vega ◽  
María Isabel Cepeda González
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Artificial Neural

The Role of Revascularisation in Patients Undergoing Non-cardiac Surgery

2010 ◽  
Vol 5 (1) ◽  
pp. 104
Author(s):  
Daniel S Menees ◽  
Eric R Bates ◽  
◽  
Keyword(s):  
Cardiac Surgery ◽  
Prediction Models ◽  
Cardiovascular Complications ◽  
Weight Of Evidence ◽  
Beta Blockade ◽  
Cardiac Morbidity ◽  
Increased Risk ◽  
Reduction Strategies ◽  
Artery Disease ◽  
Risk Reduction Strategies

Coronary artery disease (CAD) affects millions of US citizens. As the population ages, an increasing number of people with CAD are undergoing non-cardiac surgery and face significant peri-operative cardiac morbidity and mortality. Risk-prediction models can be used to help identify those patients at increased risk of peri-operative cardiovascular complications. Risk-reduction strategies utilising pharmacotherapy with beta blockade and statins have shown the most promise. Importantly, the benefit of prophylactic coronary revascularisation has not been demonstrated. The weight of evidence suggests reserving either percutaneous or surgical revascularisation in the pre-operative setting for those patients who would otherwise meet independent revascularisation criteria.

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