The prediction ability of experienced software maintainers

This study was conducted to examine the effect of Free Cash Flow Ratio, Debt Equity Ratio (DER), Institutional Ownership, Employee Welfare and Price Earning Ratio (PER) to Divident Payout Ratio (Parliament) partially on manufacturing companies listed on Indonesia Stock Exchange period 2011-2015. In addition, to test the feasibility of regression model, the influence of Free Cash Flow Ratio, Debt Equity Ratio (DER), Institutional Ownership, Employee Welfare and Price Earning Ratio (PER) to Divident Payout Ratio (DPR) simultaneously at manufacturing company listed on Bursa Indonesia Securities period 2011-2015. The population in this study are 146 manufacturing companies that have been and still listed in Indonesia Stock Exchange period 2011-2013. The sampling technique used was purposive sampling and obtained sample of 42 companies. Data analysis technique used is by using multiple linear regression test. The results showed that Free Cash Flow Ratio, no significant effect on Divident Payout Ratio (DPR). Debt Equity Ratio (DER) has a negative and significant influence on Divident Payout Ratio (DPR), Institutional Ownership has a significant positive effect on Divident Payout Ratio (DPR), Employee Welfare and Price Earning Ratio (PER) has a positive and significant influence on the Divident Payout Ratio ). Simultaneously Free Cash Flow Ratio, Debt Equity Ratio (DER), Institutional Ownership, Employee Welfare and Price Earning Ratio (PER) give effect to Divident Payout Ratio. The prediction ability of the five variables to the Divident Payout Ratio (DPR) is 21.3% as indicated by the adjusted R square of 0.271 while the remaining 79.7% is influenced by other factors not included in the research model.

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Permeability Through Bacterial Porins Dictates Whole Cell Compound Accumulation

10.26434/chemrxiv.11877834 ◽

2020 ◽

Author(s):

Silvia Acosta Gutiérrez ◽

Igor Bodrenko ◽

Matteo Ceccarelli

Keyword(s):

Cell Envelope ◽

Scoring Function ◽

Modern Medicine ◽

New Drugs ◽

Prediction Ability ◽

Whole Cell ◽

Virtual Libraries ◽

Major Bottleneck ◽

Global Threat ◽

Bacterial Porins

The lack of new drugs for Gram-negative pathogens is a global threat to modern medicine. The complexity of their cell envelope, with an additional outer membrane, hinders internal accumulation and thus, the access of molecules to targets. Our limited understanding of the molecular basis for compound influx and efflux from these pathogens is a major bottleneck for the discovery of effective antibacterial compounds. Here we analyse the correlation between the whole-cell compound accumulation of ~200 molecules and their predicted porin permeability coefficient (influx), using a recently developed scoring function. We found a strong linear relationship (75%) between the two, confirming porins key role in compound penetration. Further, the remarkable prediction ability of the scoring function demonstrates its potentiality to guide the optimization of hits to leads as well as the possibility of screening ultra-large virtual libraries. Eventually, the analysis of false positives, molecules with high-predicted influx but low accumulation, provides new hints on the molecular properties behind efflux.<br>

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A comprehensive study on statistical prediction and reduction of tire/road noise

Journal of Vibration and Control ◽

10.1177/10775463211013184 ◽

2021 ◽

pp. 107754632110131

Author(s):

Somaye Mohammadi ◽

Abdolreza Ohadi ◽

Mostafa Irannejad-Parizi

Keyword(s):

Neural Network ◽

Road Transport ◽

Relevance Vector Machine ◽

Statistical Investigation ◽

Statistical Prediction ◽

Support Vector ◽

Prediction Ability ◽

Vector Method ◽

Tire Noise ◽

Rolling Noise

Promoting safe tires with low external rolling noise increases the environmental efficiency of road transport. Although tire builders have been striving to reduce emitted noise, the issue’s sophisticated nature has made it difficult. This article aims to make the problem straightforward, relying on recent significant improvements in statistical science. In this regard, the prediction ability of new methods in this field, including support vector machine, relevance vector machine, and convolutional neural network, along with the new architecture of the neural network is compared. Tire noise is measured under the coast-by condition. Two training strategies are proposed: extracting features from a tread pattern image and directly importing an image to the model. The relevance vector method, which is trained using the first strategy, has provided the most accurate results with an error of 0.62 dB(A) in predicting the total noise level. This precise model is used instead of experimentation to analyze the sensitivity of tire noise to its parameters using a small central composite design. The parametric study reveals striking tips for reducing noise, especially in terms of interactions between parameters that have not previously been shown. Finally, a novel two-stage approach for reducing noise by tread pattern optimization is proposed, inspired by two regression models derived from statistical investigation and variance analysis. Changes in tread pattern specifications of two case studies and their randomization have resulted in a reduction of 3.2 dB(A) for a high-noise tire and 0.4 dB(A) decrement for a quieter tire.

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Study on Quality Prediction of 2219 Aluminum Alloy Friction Stir Welding Based on Real-Time Temperature Signal

Materials ◽

10.3390/ma14133496 ◽

2021 ◽

Vol 14 (13) ◽

pp. 3496

Author(s):

Haijun Wang ◽

Diqiu He ◽

Mingjian Liao ◽

Peng Liu ◽

Ruilin Lai

Keyword(s):

Aluminum Alloy ◽

Friction Stir Welding ◽

Prediction Model ◽

Real Time ◽

Support Vector ◽

Weld Quality ◽

Prediction Ability ◽

Friction Stir ◽

Temperature Signal ◽

2219 Aluminum Alloy

The online prediction of friction stir welding quality is an important part of intelligent welding. In this paper, a new method for the online evaluation of weld quality is proposed, which takes the real-time temperature signal as the main research variable. We conducted a welding experiment with 2219 aluminum alloy of 6 mm thickness. The temperature signal is decomposed into components of different frequency bands by wavelet packet method and the energy of component signals is used as the characteristic parameter to evaluate the weld quality. A prediction model of weld performance based on least squares support vector machine and genetic algorithm was established. The experimental results showed that, when welding defects are caused by a sudden perturbation during welding, the amplitude of the temperature signal near the tool rotation frequency will change significantly. When improper process parameters are used, the frequency band component of the temperature signal in the range of 0~11 Hz increases significantly, and the statistical mean value of the temperature signal will also be different. The accuracy of the prediction model reached 90.6%, and the AUC value was 0.939, which reflects the good prediction ability of the model.

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Genomic prediction ability for carcass composition indicator traits in Nellore cattle

Livestock Science ◽

10.1016/j.livsci.2021.104421 ◽

2021 ◽

Vol 245 ◽

pp. 104421

Author(s):

Rosiane P. Silva ◽

Rafael Espigolan ◽

Mariana P. Berton ◽

Raysildo B. Lôbo ◽

Cláudio U. Magnabosco ◽

...

Keyword(s):

Genomic Prediction ◽

Carcass Composition ◽

Prediction Ability ◽

Nellore Cattle ◽

Indicator Traits

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A comparison of feature extraction methods for prediction of neuropsychological scores from functional connectivity data of stroke patients

Brain Informatics ◽

10.1186/s40708-021-00129-1 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Federico Calesella ◽

Alberto Testolin ◽

Michele De Filippo De Grazia ◽

Marco Zorzi

Keyword(s):

Feature Extraction ◽

Functional Connectivity ◽

Dimensionality Reduction ◽

Verbal Memory ◽

Resting State ◽

Component Analysis ◽

Model Complexity ◽

Stroke Patients ◽

Prediction Ability ◽

The Impact

AbstractMultivariate prediction of human behavior from resting state data is gaining increasing popularity in the neuroimaging community, with far-reaching translational implications in neurology and psychiatry. However, the high dimensionality of neuroimaging data increases the risk of overfitting, calling for the use of dimensionality reduction methods to build robust predictive models. In this work, we assess the ability of four well-known dimensionality reduction techniques to extract relevant features from resting state functional connectivity matrices of stroke patients, which are then used to build a predictive model of the associated deficits based on cross-validated regularized regression. In particular, we investigated the prediction ability over different neuropsychological scores referring to language, verbal memory, and spatial memory domains. Principal Component Analysis (PCA) and Independent Component Analysis (ICA) were the two best methods at extracting representative features, followed by Dictionary Learning (DL) and Non-Negative Matrix Factorization (NNMF). Consistent with these results, features extracted by PCA and ICA were found to be the best predictors of the neuropsychological scores across all the considered cognitive domains. For each feature extraction method, we also examined the impact of the regularization method, model complexity (in terms of number of features that entered in the model) and quality of the maps that display predictive edges in the resting state networks. We conclude that PCA-based models, especially when combined with L1 (LASSO) regularization, provide optimal balance between prediction accuracy, model complexity, and interpretability.

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Usefulness of the PRECISE DAPT score as a predictor of ischaemic stroke after an acute coronary syndrome

European Heart Journal ◽

10.1093/ehjci/ehaa946.1616 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

T Gonzalez Ferrero ◽

B.A.A Alvarez Alvarez ◽

C.C.A Cacho Antonio ◽

M.P.D Perez Dominguez ◽

P.A.M Antunez Muinos ◽

...

Keyword(s):

Acute Coronary Syndrome ◽

Ischaemic Stroke ◽

Independent Predictor ◽

Multivariable Logistic Regression Analysis ◽

Funding Source ◽

Independent Factor ◽

Prediction Ability ◽

Coronary Syndrome ◽

Grace Score

Abstract Introduction Ischaemic stroke (IS) risk after acute coronary syndrome is increasing. The aim of our study was to evaluate the stroke rate in a multicentre study and to determine the prediction ability of the PRECISE DAPT score, added to the prediction power of the GRACE score, already demonstrated. Methods This was a retrospective study, carried out in two centres with 5916 patients, with ACS discharged between 2011 and 2017 (median 66±13 years, 27.7% women). The primary endpoint was the occurrence of ischaemic stroke and its risk during follow up (median 5.5, IQR 2.6–7.0). Results A multivariable logistic regression analysis was made, where GRACE (HR 1.01, IC 95% 1.00–1.02) and PRECISE DAPT score (HR 1.03, IC 95% 1.01–1.05) were both an independent predictor of ischaemic stroke after ACS, in a model adjusted by age and AF, which was found to be the independent factor with highest risk (HR 1.67, IC 95% 1.09–2.55). Conclusions GRACE and PRECISE DAPT scores are ischaemic stroke predictors used during follow-up for patients after acute coronary syndrome. We should use both of them not only trying to predict ischaemic/haemorrhagic risk respectively but also as ischaemic stroke predictors. Figure 1. AUC Curves Funding Acknowledgement Type of funding source: None

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Integration of enzyme constraints in a genome-scale metabolic model of Aspergillus niger improves phenotype predictions

Microbial Cell Factories ◽

10.1186/s12934-021-01614-2 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Jingru Zhou ◽

Yingping Zhuang ◽

Jianye Xia

Keyword(s):

Aspergillus Niger ◽

Large Scale ◽

Measurement Techniques ◽

Metabolic Model ◽

System Level ◽

Metabolic Phenotype ◽

Omics Data ◽

Prediction Ability ◽

Phenotype Prediction ◽

Genome Scale

Abstract Background Genome-scale metabolic model (GSMM) is a powerful tool for the study of cellular metabolic characteristics. With the development of multi-omics measurement techniques in recent years, new methods that integrating multi-omics data into the GSMM show promising effects on the predicted results. It does not only improve the accuracy of phenotype prediction but also enhances the reliability of the model for simulating complex biochemical phenomena, which can promote theoretical breakthroughs for specific gene target identification or better understanding the cell metabolism on the system level. Results Based on the basic GSMM model iHL1210 of Aspergillus niger, we integrated large-scale enzyme kinetics and proteomics data to establish a GSMM based on enzyme constraints, termed a GEM with Enzymatic Constraints using Kinetic and Omics data (GECKO). The results show that enzyme constraints effectively improve the model’s phenotype prediction ability, and extended the model’s potential to guide target gene identification through predicting metabolic phenotype changes of A. niger by simulating gene knockout. In addition, enzyme constraints significantly reduced the solution space of the model, i.e., flux variability over 40.10% metabolic reactions were significantly reduced. The new model showed also versatility in other aspects, like estimating large-scale $$k_{{cat}}$$ k cat values, predicting the differential expression of enzymes under different growth conditions. Conclusions This study shows that incorporating enzymes’ abundance information into GSMM is very effective for improving model performance with A. niger. Enzyme-constrained model can be used as a powerful tool for predicting the metabolic phenotype of A. niger by incorporating proteome data. In the foreseeable future, with the fast development of measurement techniques, and more precise and rich proteomics quantitative data being obtained for A. niger, the enzyme-constrained GSMM model will show greater application space on the system level.

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Incidence trends and survival prediction of urothelial cancer of the bladder: a population-based study

World Journal of Surgical Oncology ◽

10.1186/s12957-021-02327-x ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Hairong He ◽

Tianjie Liu ◽

Didi Han ◽

Chengzhuo Li ◽

Fengshuo Xu ◽

...

Keyword(s):

Urothelial Cancer ◽

Predictive Accuracy ◽

Population Based ◽

Calibration Plot ◽

Survival Prediction ◽

Operating Characteristics ◽

Prediction Ability ◽

Term Survival ◽

Incidence Trends ◽

Cancer Of The Bladder

Abstract Background The aim of this study is to determine the incidence trends of urothelial cancer of the bladder (UCB) and to develop a nomogram for predicting the cancer-specific survival (CSS) of postsurgery UCB at a population-based level based on the SEER database. Methods The age-adjusted incidence of UCB diagnosed from 1975 to 2016 was extracted, and its annual percentage change was calculated and joinpoint regression analysis was performed. A nomogram was constructed for predicting the CSS in individual cases based on independent predictors. The predictive performance of the nomogram was evaluated using the consistency index (C-index), net reclassification index (NRI), integrated discrimination improvement (IDI), a calibration plot and the receiver operating characteristics (ROC) curve. Results The incidence of UCB showed a trend of first increasing and then decreasing from 1975 to 2016. However, the overall incidence increased over that time period. The age at diagnosis, ethnic group, insurance status, marital status, differentiated grade, AJCC stage, regional lymph nodes removed status, chemotherapy status, and tumor size were independent prognostic factors for postsurgery UCB. The nomogram constructed based on these independent factors performed well, with a C-index of 0.823 and a close fit to the calibration curve. Its prediction ability for CSS of postsurgery UCB is better than that of the existing AJCC system, with NRI and IDI values greater than 0 and ROC curves exhibiting good performance for 3, 5, and 8 years of follow-up. Conclusions The nomogram constructed in this study might be suitable for clinical use in improving the clinical predictive accuracy of the long-term survival for postsurgery UCB.

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Remote Sensing Estimation of Bamboo Forest Aboveground Biomass Based on Geographically Weighted Regression

Remote Sensing ◽

10.3390/rs13152962 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2962

Author(s):

Jingyi Wang ◽

Huaqiang Du ◽

Xuejian Li ◽

Fangjie Mao ◽

Meng Zhang ◽

...

Keyword(s):

Aboveground Biomass ◽

Geographically Weighted Regression ◽

Local Variation ◽

Zhejiang Province ◽

Weighted Regression ◽

Landsat 8 ◽

Bamboo Forest ◽

Prediction Ability ◽

Gwr Model ◽

Forest Aboveground Biomass

Bamboo forests are widespread in subtropical areas and are well known for their rapid growth and great carbon sequestration ability. To recognize the potential roles and functions of bamboo forests in regional ecosystems, forest aboveground biomass (AGB)—which is closely related to forest productivity, the forest carbon cycle, and, in particular, carbon sinks in forest ecosystems—is calculated and applied as an indicator. Among the existing studies considering AGB estimation, linear or nonlinear regression models are the most frequently used; however, these methods do not take the influence of spatial heterogeneity into consideration. A geographically weighted regression (GWR) model, as a spatial local model, can solve this problem to a certain extent. Based on Landsat 8 OLI images, we use the Random Forest (RF) method to screen six variables, including TM457, TM543, B7, NDWI, NDVI, and W7B6VAR. Then, we build the GWR model to estimate the bamboo forest AGB, and the results are compared with those of the cokriging (COK) and orthogonal least squares (OLS) models. The results show the following: (1) The GWR model had high precision and strong prediction ability. The prediction accuracy (R2) of the GWR model was 0.74, 9%, and 16% higher than the COK and OLS models, respectively, while the error (RMSE) was 7% and 12% lower than the errors of the COK and OLS models, respectively. (2) The bamboo forest AGB estimated by the GWR model in Zhejiang Province had a relatively dense spatial distribution in the northwestern, southwestern, and northeastern areas. This is in line with the actual bamboo forest AGB distribution in Zhejiang Province, indicating the potential practical value of our study. (3) The optimal bandwidth of the GWR model was 156 m. By calculating the variable parameters at different positions in the bandwidth, close attention is given to the local variation law in the estimation of the results in order to reduce the model error.

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