A novel astrophysics-based framework for prediction of binding affinity of glucose binder

2020 ◽  
Vol 34 (31) ◽  
pp. 2050346
Author(s):  
Rajesh Kondabala ◽  
Vijay Kumar ◽  
Amjad Ali ◽  
Manjit Kaur
Keyword(s):  
Decision Tree ◽  
Binding Affinity ◽  
Cross Validation ◽  
Learning Strategy ◽  
Experimental Results ◽  
The Other ◽  
Computational Time ◽  
Glucose Binding ◽  
Regression Algorithms

In this paper, a novel astrophysics-based prediction framework is developed for estimating the binding affinity of a glucose binder. The proposed framework utilizes the molecule properties for predicting the binding affinity. It also uses the astrophysics-learning strategy that incorporates the concepts of Kepler’s law during the prediction process. The proposed framework is compared with 10 regression algorithms over ZINC dataset. Experimental results reveal that the proposed framework provides 99.30% accuracy of predicting binding affinity. However, decision tree provides the prediction with 97.14% accuracy. Cross-validation results show that the proposed framework provides better accuracy than the other existing models. The developed framework enables researchers to screen glucose binder rapidly. It also reduces computational time for designing small glucose binding molecule.

Assessment of Cavitation Models for Flows in Diesel Injectors With Single- and Two-Fluid Approaches

2015 ◽  
Vol 138 (1) ◽  
Author(s):  
Kaushik Saha ◽  
Xianguo Li
Keyword(s):  
Fluid Model ◽  
Injection Pressure ◽  
Experimental Results ◽  
The Other ◽  
Computational Time ◽  
Two Phase ◽  
Numerical Predictions ◽  
Mass Flow Rates ◽  
Two Phases ◽  
Two Fluid

Several recent cavitation models for the analysis of two-phase flows in diesel injectors with single- and two-fluid modeling approaches have been evaluated, including the Saha–Abu-Ramandan–Li (SAL), Schnerr–Sauer (SS), and Zwart–Gerber–Belamri (ZGB) models. The SAL model is a single-fluid model, while the other two models have been implemented with both single- and two-fluid approaches. Numerical predictions are compared with experimental results available in literature, qualitatively with experimental images of two-phase flow in an optically accessible nozzle, and quantitatively with measured mass flow rates and velocity profiles. It is found that at low injection pressure differentials there can be considerable discrepancy in the predictions of the vapor distribution from the three models considered. This discrepancy is reduced as the injection pressure differential is increased. Implementation of the SS and ZGB models with single- and two- fluid approaches yields noticeable differences in the results because of the relative velocity between the two phases, with two-fluid approach providing better agreement with experimental results. The performance of the SS and ZGB models implemented with the two-fluid approach is comparable with the SAL single-fluid model, but with significantly more computational time. Overall, the SAL single-fluid model performs comparatively better with respect to the other two models.

MISS ONE OUT: A CROSS-VALIDATION METHOD UTILIZING INDUCED TEACHER NOISE

2013 ◽  
Vol 27 (07) ◽  
pp. 1351003
Author(s):  
AHMET ALPTEKIN ◽  
OLCAY KURSUN
Keyword(s):  
Cross Validation ◽  
Training Sample ◽  
Experimental Results ◽  
The Other ◽  
Training Set ◽  
Test Set ◽  
Validation Method ◽  
Trained Classifier ◽  
Benchmark Datasets ◽  
Leave One Out

Leave-one-out (LOO) and its generalization, K-Fold, are among most well-known cross-validation methods, which divide the sample into many folds, each one of which is, in turn, left out for testing, while the other parts are used for training. In this study, as an extension of this idea, we propose a new cross-validation approach that we called miss-one-out (MOO) that mislabels the example(s) in each fold and keeps this fold in the training set as well, rather than leaving it out as LOO does. Then, MOO tests whether the trained classifier can correct the erroneous label of the training sample. In principle, having only one fold deliberately labeled incorrectly should have only a small effect on the classifier that uses this bad-fold along with K - 1 good folds and can be utilized as a generalization measure of the classifier. Experimental results on a number of benchmark datasets and three real bioinformatics dataset show that MOO can better estimate the test set accuracy of the classifier.

PREDIKSI KUALITAS AIR SUNGAI CILIWUNG DENGAN MENGGUNAKAN ALGORITMA POHON KEPUTUSAN

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
Mohammad Haekal ◽  
Henki Bayu Seta ◽  
Mayanda Mega Santoni
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Cross Validation ◽  
Online Monitoring ◽  
Training Set ◽  
Microsoft Excel ◽  
Test Set

Untuk memprediksi kualitas air sungai Ciliwung, telah dilakukan pengolahan data-data hasil pemantauan secara Online Monitoring dengan menggunakan Metode Data Mining. Pada metode ini, pertama-tama data-data hasil pemantauan dibuat dalam bentuk tabel Microsoft Excel, kemudian diolah menjadi bentuk Pohon Keputusan yang disebut Algoritma Pohon Keputusan (Decision Tree) mengunakan aplikasi WEKA. Metode Pohon Keputusan dipilih karena lebih sederhana, mudah dipahami dan mempunyai tingkat akurasi yang sangat tinggi. Jumlah data hasil pemantauan kualitas air sungai Ciliwung yang diolah sebanyak 5.476 data. Hasil klarifikasi dengan Pohon Keputusan, dari 5.476 data ini diperoleh jumlah data yang mengindikasikan sungai Ciliwung Tidak Tercemar sebanyak 1.059 data atau sebesar 19,3242%, dan yang mengindikasikan Tercemar sebanyak 4.417 data atau 80,6758%. Selanjutnya data-data hasil pemantauan ini dievaluasi menggunakan 4 Opsi Tes (Test Option) yaitu dengan Use Training Set, Supplied Test Set, Cross-Validation folds 10, dan Percentage Split 66%. Hasil evaluasi dengan 4 opsi tes yang digunakan ini, semuanya menunjukkan tingkat akurasi yang sangat tinggi, yaitu diatas 99%. Dari data-data hasil peneltian ini dapat diprediksi bahwa sungai Ciliwung terindikasi sebagai sungai tercemar bila mereferensi kepada Peraturan Pemerintah Republik Indonesia nomor 82 tahun 2001 dan diketahui pula bahwa penggunaan aplikasi WEKA dengan Algoritma Pohon Keputusan untuk mengolah data-data hasil pemantauan dengan mengambil tiga parameter (pH, DO dan Nitrat) adalah sangat akuran dan tepat. Kata Kunci : Kualitas air sungai, Data Mining, Algoritma Pohon Keputusan, Aplikasi WEKA.

The Influence of Water Regime on the Performance of Aquatic Plants

1994 ◽  
Vol 29 (4) ◽  
pp. 127-132 ◽  
Author(s):  
Naomi Rea ◽  
George G. Ganf
Keyword(s):  
Deep Water ◽  
Aquatic Plants ◽  
Water Regime ◽  
Optimal Performance ◽  
Experimental Results ◽  
The Other ◽  
Full Potential ◽  
Influence Of Water ◽  
Emergent Species

Experimental results demonstrate bow small differences in depth and water regime have a significant affect on the accumulation and allocation of nutrients and biomass. Because the performance of aquatic plants depends on these factors, an understanding of their influence is essential to ensure that systems function at their full potential. The responses differed for two emergent species, indicating that within this morphological category, optimal performance will fall at different locations across a depth or water regime gradient. The performance of one species was unaffected by growth in mixture, whereas the other performed better in deep water and worse in shallow.

Mining discriminative patches for script identification in natural scene images

2021 ◽  
Vol 40 (1) ◽  
pp. 551-563
Author(s):  
Liqiong Lu ◽  
Dong Wu ◽  
Ziwei Tang ◽  
Yaohua Yi ◽  
Faliang Huang
Keyword(s):  
Neural Networks ◽  
Experimental Results ◽  
The Other ◽  
Natural Scene ◽  
Fixed Size ◽  
Script Identification ◽  
Aspect Ratios ◽  
Novel Approach ◽  
Public Datasets ◽  
Natural Scene Images

This paper focuses on script identification in natural scene images. Traditional CNNs (Convolution Neural Networks) cannot solve this problem perfectly for two reasons: one is the arbitrary aspect ratios of scene images which bring much difficulty to traditional CNNs with a fixed size image as the input. And the other is that some scripts with minor differences are easily confused because they share a subset of characters with the same shapes. We propose a novel approach combing Score CNN, Attention CNN and patches. Attention CNN is utilized to determine whether a patch is a discriminative patch and calculate the contribution weight of the discriminative patch to script identification of the whole image. Score CNN uses a discriminative patch as input and predict the score of each script type. Firstly patches with the same size are extracted from the scene images. Secondly these patches are used as inputs to Score CNN and Attention CNN to train two patch-level classifiers. Finally, the results of multiple discriminative patches extracted from the same image via the above two classifiers are fused to obtain the script type of this image. Using patches with the same size as inputs to CNN can avoid the problems caused by arbitrary aspect ratios of scene images. The trained classifiers can mine discriminative patches to accurately identify some confusing scripts. The experimental results show the good performance of our approach on four public datasets.

scholarly journals DV-DVFS: merging data variety and DVFS technique to manage the energy consumption of big data processing

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Hossein Ahmadvand ◽  
Fouzhan Foroutan ◽  
Mahmood Fathy
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Processing Time ◽  
Experimental Results ◽  
The Other ◽  
Data Sets ◽  
Multiple Sources ◽  
Evaluation Phase ◽  
Dynamic Voltage ◽  
Processing Resources

AbstractData variety is one of the most important features of Big Data. Data variety is the result of aggregating data from multiple sources and uneven distribution of data. This feature of Big Data causes high variation in the consumption of processing resources such as CPU consumption. This issue has been overlooked in previous works. To overcome the mentioned problem, in the present work, we used Dynamic Voltage and Frequency Scaling (DVFS) to reduce the energy consumption of computation. To this goal, we consider two types of deadlines as our constraint. Before applying the DVFS technique to computer nodes, we estimate the processing time and the frequency needed to meet the deadline. In the evaluation phase, we have used a set of data sets and applications. The experimental results show that our proposed approach surpasses the other scenarios in processing real datasets. Based on the experimental results in this paper, DV-DVFS can achieve up to 15% improvement in energy consumption.

Study of wave attenuation in concrete

1993 ◽  
Vol 8 (9) ◽  
pp. 2344-2353 ◽  
Author(s):  
J-M. Berthelot ◽  
Souda M. Ben ◽  
J.L. Robert
Keyword(s):  
Experimental Study ◽  
Plane Wave ◽  
Wave Attenuation ◽  
Plane Waves ◽  
Propagation Distance ◽  
Experimental Results ◽  
The Other ◽  
Wave Frequency ◽  
Concrete Composition ◽  
Analytical Expression

The experimental study of wave attenuation in concrete has been achieved in the case of the propagation of plane waves in concrete rods. Different mortars and concretes have been investigated. A transmitter transducer coupled to one of the ends of the concrete rod generates the propagation of a plane wave in the rod. The receiver transducer, similar to the previous one, is coupled to the other end of the rod. The experimental results lead to an analytical expression for wave attenuation as function of the concrete composition, the propagation distance, and the wave frequency.

scholarly journals GazeEMD: Detecting Visual Intention in Gaze-Based Human-Robot Interaction

Robotics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 68
Author(s):  
Lei Shi ◽  
Cosmin Copot ◽  
Steve Vanlanduit
Keyword(s):  
Cognitive Load ◽  
Real World ◽  
Robotic Manipulator ◽  
Similarity Score ◽  
Human Robot Interaction ◽  
Experimental Results ◽  
The Other ◽  
Robot Interaction ◽  
Run Length ◽  
The Real

In gaze-based Human-Robot Interaction (HRI), it is important to determine human visual intention for interacting with robots. One typical HRI interaction scenario is that a human selects an object by gaze and a robotic manipulator will pick up the object. In this work, we propose an approach, GazeEMD, that can be used to detect whether a human is looking at an object for HRI application. We use Earth Mover’s Distance (EMD) to measure the similarity between the hypothetical gazes at objects and the actual gazes. Then, the similarity score is used to determine if the human visual intention is on the object. We compare our approach with a fixation-based method and HitScan with a run length in the scenario of selecting daily objects by gaze. Our experimental results indicate that the GazeEMD approach has higher accuracy and is more robust to noises than the other approaches. Hence, the users can lessen cognitive load by using our approach in the real-world HRI scenario.

Natural Vulcanization Accelerators in Hevea Latex

1948 ◽  
Vol 21 (4) ◽  
pp. 853-859
Author(s):  
R. F. A. Altman
Keyword(s):  
Amino Acids ◽  
Experimental Results ◽  
The Other ◽  
Active Nitrogen ◽  
Nitrogen Bases ◽  
Other Hand ◽  
Decomposition Processes ◽  
Volatile Products ◽  
Nitrogenous Substances ◽  
The One

Abstract As numerous investigators have shown, some of the nonrubber components of Hevea latex have a decided accelerating action on the process of vulcanization. A survey of the literature on this subject points to the validity of certain general facts. 1. Among the nonrubber components of latex which have been investigated, certain nitrogenous bases appear to be most important for accelerating the rate of vulcanization. 2. These nitrogen bases apparently occur partly naturally in fresh latex, and partly as the result of putrefaction, heating, and other decomposition processes. 3. The nitrogen bases naturally present in fresh latex at later stages have been identified by Altman to be trigonelline, stachhydrine, betonicine, choline, methylamine, trimethylamine, and ammonia. These bases are markedly active in vulcanization, as will be seen in the section on experimental results. 4. The nitrogenous substances formed by the decomposition processes have only partly been identified, on the one hand as tetra- and pentamethylene diamine and some amino acids, on the other hand as alkaloids, proline, diamino acids, etc. 5. It has been generally accepted that these nitrogenous substances are derived from the proteins of the latex. 6. Decomposition appears to be connected with the formation of a considerable amount of acids. 7. The production of volatile nitrogen bases as a rule accompanies the decomposition processes. These volatile products have not been identified. 8. The active nitrogen bases, either already formed or derived from complex nitrogenous substances, seem to be soluble in water but only slightly soluble in acetone.

Screening of Coagulant for Washing Wastewater Treatment

2015 ◽  
Vol 1092-1093 ◽  
pp. 972-975
Author(s):  
Jing Yang
Keyword(s):  
Wastewater Treatment ◽  
Removal Rate ◽  
Experimental Results ◽  
The Other ◽  
Aluminium Chloride ◽  
Coagulation Tests ◽  
Cyclic Utilization ◽  
Washing Wastewater ◽  
Better Than

According to the problems exist in cyclic utilization of washing wastewater, the coagulation tests utilizing ferric trichloride (FeCl3), alums, poly aluminium chloride (PAC) and polyacrylamide (PAM) are studied, respectively. Experimental results show that PAC was much better than the other coagulants in the removal of LAS and chroma as a single coagulant. Cast 2.5mL PAC(10%) into quantitative washing wastewater, the removal rate of LAS and chroma reach 82.5% and 87.8%, respectively. When mix the every two kinds of coagulants, maintaining the same total amount of coagulant to 2.5mL, cast1.0mL PAC(10%) and 1.5mL alum (10%) into washing wastewater ,the removal rate of LAS and chroma reach 84.1% and 90.0%, respectively.

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