scholarly journals Bruise susceptibilities of kiwifruit as affected by impact and fruit properties

2012 ◽  
Vol 58 (No. 3) ◽  
pp. 107-113 ◽  
Author(s):  
E. Ahmadi
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Fruit Quality ◽  
Prediction Models ◽  
Radius Of Curvature ◽  
Regression Analyses ◽  
Probability Level ◽  
Absorbed Energy ◽  
Bruise Susceptibility ◽  
Damage Susceptibility

Kiwifruit bruise damage is a common postharvest disorder that substantially reduces fruit quality and marketability. Fruit bruise cause tissue softening and make them more susceptible to undesired agents such as diseases-inducing agents. Factors that affect kiwifruit bruise susceptibility such as impact properties and fruit properties were investigated. Two bruise prediction models were constructed for the damage susceptibility of kiwifruit (measured by absorbed energy) using multiple linear regression analyses. Kiwifruits were subjected to dynamic loading by means of a pendulum at three levels of impact. Significant effects of acoustic stiffness, temperature and the radius of curvature and some interactions on bruising were obtained at 5% probability level.

scholarly journals Prediction of Blended Yarn Evenness and Tensile Properties by Using Artificial Neural Network and Multiple Linear Regression

2016 ◽  
Vol 16 (2) ◽  
pp. 43-50 ◽  
Author(s):  
Samander Ali Malik ◽  
Assad Farooq ◽  
Thomas Gereke ◽  
Chokri Cherif
Keyword(s):  
Neural Networks ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Tensile Properties ◽  
Regression Models ◽  
Prediction Models ◽  
Research Work ◽  
Absolute Error ◽  
Knowledge Domain ◽  
Artificial Neural

Abstract The present research work was carried out to develop the prediction models for blended ring spun yarn evenness and tensile parameters using artificial neural networks (ANNs) and multiple linear regression (MLR). Polyester/cotton blend ratio, twist multiplier, back roller hardness and break draft ratio were used as input parameters to predict yarn evenness in terms of CVm% and yarn tensile properties in terms of tenacity and elongation. Feed forward neural networks with Bayesian regularisation support were successfully trained and tested using the available experimental data. The coefficients of determination of ANN and regression models indicate that there is a strong correlation between the measured and predicted yarn characteristics with an acceptable mean absolute error values. The comparative analysis of two modelling techniques shows that the ANNs perform better than the MLR models. The relative importance of input variables was determined using rank analysis through input saliency test on optimised ANN models and standardised coefficients of regression models. These models are suitable for yarn manufacturers and can be used within the investigated knowledge domain.

scholarly journals Thyroid Function Changes and Pubertal Progress in Females: A Longitudinal Study in Iodine-Sufficient Areas of East China

2021 ◽  
Vol 12 ◽  
Author(s):  
Yingying Wang ◽  
Dandan He ◽  
Chaowei Fu ◽  
Xiaolian Dong ◽  
Feng Jiang ◽  
...  
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Thyroid Function ◽  
Pubertal Development ◽  
Thyroid Stimulating Hormone ◽  
East China ◽  
Regression Analyses ◽  
Free Triiodothyronine ◽  
Serum Tsh

BackgroundThe onset of puberty is influenced by thyroid function, and thyroid hormones (THs) fluctuate substantially during the period of pubertal development. However, it needs to be further clarified how THs change at specific puberty stages and how it influences pubertal development in girls. So far, longitudinal data from China are scarce.MethodsA cohort study was conducted among girls during puberty in iodine-sufficient regions of East China between 2017 to 2019. Serum thyroid stimulating hormone (TSH), free triiodothyronine (FT3), and free thyroxine (FT4) were determined for each participant. Thyroid homeostasis structure parameters (THSPs), including the ratio of FT4 to FT3 (FT4/FT3), Jostel’s TSH index (TSHI), and thyroid feedback quantile-based index (TFQI), were calculated. Puberty category scores (PCS), calculated based on the Puberty Development Scale (PDS), was used to assess the stage of puberty. Girls were grouped into three categories according to PCS changes (△PCS) and six categories according puberty stage (BPFP: pre-pubertal at both baseline and follow-up; BPFL: pre-pubertal at baseline and late-pubertal at follow-up, respectively; BPFT: pre-pubertal at baseline and post-pubertal at follow-up, respectively; BLFL: late-pubertal at both baseline and follow-up; BLFT: late-pubertal at baseline and post-pubertal at follow-up, respectively; BTFT: post-pubertal at both baseline and follow-up). Multiple linear regression analyses were used to evaluate the associations of THs changes with pubertal progress.ResultsThe levels of serum TSH and FT3 decreased while serum FT4 increased during the study period (P<0.001). In multiple linear regression analyses, after adjustment for covariables, FT3 decreased by an additional 0.24 pmol/L (95% CI: -0.47 to -0.01) in the higher △PCS group than the lower △PCS group. Compared with the BLFL group, the BPFT group showed an additional decline in FT3 (β= -0.39 pmol/L, 95%CI: -0.73 to -0.04), the BTFT group showed a lower decline in TSH (β=0.50 mU/L, 95% CI: 0.21 to 0.80) and a lower decline in TSHI (β=0.24, 95%CI: 0.06 to 0.41), respectively. There was no association of △FT4 or △TFQI with △PCS or the puberty pattern.ConclusionsSerum TSH and FT3 decreased while serum FT4 increased among girls during puberty. Both the initial stage and the velocity of pubertal development were related to thyroid hormone fluctuations.

Anxiety sensitivity, disgust sensitivity and aversive reactions to four stimuli

2020 ◽  
pp. 1-12
Author(s):  
Jessica R. Winder ◽  
Katie H. Mangen ◽  
Anna E. Martinez-Snyder ◽  
David P. Valentiner
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Anxiety Sensitivity ◽  
Regression Analyses ◽  
Disgust Sensitivity ◽  
Specific Phobias

Abstract Background: Prior theory and research has implicated disgust as relevant to some, but not all phobias. Aims: The current study examined whether anxiety sensitivity is more relevant to certain specific phobias and whether disgust sensitivity is more relevant to other specific phobias. Method: Participants (n = 201) completed measures of anxiety sensitivity, disgust sensitivity and measures of aversive reactions in the presence of two fear-relevant stimuli (i.e. heights and small, enclosed spaces) and two disgust-relevant stimuli (i.e. spiders and blood/injury). Results: Results of multiple linear regression analyses revealed that disgust sensitivity showed significant associations with aversive reactions in all four stimulus domains after controlling for anxiety sensitivity. After controlling for disgust sensitivity, anxiety sensitivity showed associations with the two fear-relevant phobias but not with the two disgust-relevant phobias included in this study. Anxiety sensitivity also showed an association with variance specific to one of the two fear-relevant specific phobias included in the study. Disgust sensitivity also showed associations with variance specific to both of the disgust-relevant phobias included in the study but not with variance specific to either of the fear-relevant specific phobias. Conclusions: These results provide evidence that the distinction between fear-relevant and disgust-relevant specific phobias is meaningful and also implicate disgust sensitivity as relevant to aversive reactions to all stimuli included in this study.

On the Multiple Linear Regression and Artificial Neural Networks for Strength Prediction of Soil-Based Controlled Low-Strength Material

2014 ◽  
Vol 597 ◽  
pp. 349-352 ◽  
Author(s):  
Li Jeng Huang ◽  
Yeong Nain Sheen ◽  
Duc Hien Le
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Prediction Models ◽  
Laboratory Data ◽  
Pulse Velocity ◽  
Strength Prediction ◽  
Strength Material ◽  
Artificial Neural ◽  
Low Strength ◽  
Controlled Low Strength Material

This paper presents two approaches, multiple linear regression (MLR) and artificial neural network (ANN), to develop predictive models for unconfined compressive strength of soil-based controlled low-strength material (CLSM). Our obtained laboratory data conducting on the soil-based CLSM were employed for analysis. Two strength prediction models were proposed: (1) strength is assumed to be a function of mix proportion and curing period; and (2) it is estimated from measured ultrasonic pulse velocity combined with effect of mixture parameters and curing ages. In each model, three predicted formulas were developed; one from MLR and two from ANN. It was showed that all the proposed equations have a well-predicted capacity.

scholarly journals PENGARUH NON PERFORMING LOAN (NPL) DAN LOAN TO DEPOSIT RATIO (LDR) TERHADAP RETURN ON ASSET (ROA) PADA PERUSAHAAN PERBANKAN YANG TERDAFTAR DI BURSA EFEK INDONESIA

2020 ◽  
pp. 220-230
Author(s):  
Randi Siregar ◽  
Sabeth Sembiring
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Quantitative Research ◽  
Negative Impact ◽  
Coefficient Of Determination ◽  
Regression Analyses ◽  
Independent Variables ◽  
Significant Negative Impact ◽  
Negative Effect ◽  
Positive Effect

The purpose of this research was conducted to know the influence of: (1) Simultaneous Non Performing Loan and Loan to Deposit Ratio against Return On Asset, (2) Partial Non-Performing Loan against Return On Asset and (3) partial Loan to Deposit against Return On Asset Corporate Banking the Go Public period 2010 – 2012.The research uses quantitative research plans. The research subject is a banking company that go public from 2014 – 2016 and the objects are Non Performing Loan, Loan to Deposit Ratio and Return On Asset.Data collected using documentation methods and analyzed with multiple linear regression analyses. The results showed that (1) Non Performing Loan and Loan to Deposit Ratio simultaneously had significant effect on Return On Asset.(2) Non Performing Loan partially negative effect on Return On Asset, (3) Loan to Deposit Ratio positively affects the Return On Asset of the banking company listed on the Indonesia Stock Exchange.The value of the coefficient of determination (adjusted R2) of 0.323 or 32.3%, this translates to 32.3% of ROA variations that can be explained by variations of independent variables of the NPL and LDR.The remaining 67.7% is explained by other reasons outside of the regression model.Based on the results of multiple linear regression analyses indicating that the NPL has a significant negative impact on ROA, LDR has a significant and positive effect on Roa, NPL and LDR.

scholarly journals Social Jetlag Is Associated With Impaired Metabolic Control During a 1-Year Follow-Up

2021 ◽  
Vol 12 ◽  
Author(s):  
Maria Carliana Mota ◽  
Catarina Mendes Silva ◽  
Laura Cristina Tibiletti Balieiro ◽  
Walid Makin Fahmy ◽  
Elaine Cristina Marqueze ◽  
...  
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Metabolic Control ◽  
Fasting Glucose ◽  
Sleep Time ◽  
Metabolic Parameters ◽  
Absolute Difference ◽  
Regression Analyses ◽  
Social Jetlag

Previous studies have identified social jetlag (SJL) as a risk factor for non-communicable chronic diseases (NCCDs), but its association with metabolic control over time is unclear in the literature. Therefore, we examined the influence of SJL on metabolic parameters and blood pressure (BP) in patients with NCCDs over a 1-year follow-up. This retrospective, longitudinal study included 625 individuals (age: 56.0 +12.0 years; 76% female) with NCCDs [type 2 diabetes mellitus (TD2), systemic arterial hypertension (SHA), obesity, or dyslipidemia]. SJL was calculated based on the absolute difference between mid-sleep time on weekends and weekdays. Current metabolic parameters and BP of the patients were compared with data from a year prior. Generalized estimating equations (GEE) and multiple linear regression analyses were used to examine the association among SJL, metabolic parameters, and BP. Multiple linear regression analyses adjusted for confounders showed that SJL was positively associated with the delta difference of fasting glucose (β = 0.11, p = 0.02) and triglyceride levels (β = 0.09, p = 0.04) among all subjects with NCCDs, and with fasting glucose (β = 0.30, p = 0.0001) and triglyceride levels (β = 0.22, p = 0.01) in the TD2 group. GEE analysis demonstrated an isolated effect of SJL on diastolic BP. High SJL impaired clinical and metabolic control in individuals with NCCDs, leading to a worse profile after a 1-year follow-up, particularly among type II diabetics.

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