# Prediction Equation

### Identification of PPARgamma partial agonists of natural origin (II): in silico prediction in natural extracts with known antidiabetic activity.

... Identification of PPARgamma Partial Agonists of Natural
Origin (II): In Silico

**Prediction**in Natural Extracts with Known Antidiabetic Activity Laura Guasch1, Esther Sala1, Miquel Mulero1, Cristina Valls1, Maria Josepa Salvadó1, Gerard Pujadas1,2, Santiago ... type 2 Diabetes Mellitus. Citation: Guasch L, Sala E, Mulero M, Valls C, Salvadó MJ, et al. (2013) Identification of PPARgamma Partial Agonists of Natural Origin (II): In Silico**Prediction**in Natural Extracts with Known Antidiabetic Activity. PLoS ONE 8(2): e55889. doi:10.1371/journal.pone.0055889 Editor: Vladimir N. Uversky, University of South Florida ... doi:10.1371/journal.pone.0044971. 42. Guasch L, Sala E, Ojeda MJ, Valls C, Bladé C, et al. (2012) Identification of novel human dipeptidyl peptidase-IV inhibitors of natural origin (Part II): in silico**prediction**in antidiabetic extracts. PLoS ONE 7: e44972. doi:10.1371/ journal.pone.0044972. 43. Guex N, Peitsch MC (1997) SWISS-MODEL and the Swiss-PdbViewer: an environment for 0 2 10

### Force prediction in cold rolling mills by polynomial methods

... deviations
compensation.
115
THE ANNALS OF “DUNAREA DE JOS” UNIVERSITY OF GALATI
FASCICLE III, 2007 ISSN 1221-454X
Minimum variance control implies a

**prediction**model. 3. (8) ∆Fd = Λ ⋅ ∆F ∧ ∆F = H ⋅ ∆ F The**prediction**of ∆F with λ sample periods is allows the compensation of lag time introduced by hydraulic system which compensate the thickness deviations. ... the filter H which approximate the non causal**prediction**function: A=A A (19) The least square error is: Let ∆F(t) be a stationary process with zero mean value and autocorrelation function: + - (18) The error autocorrelation function is: where Re is the autocorrelation function of white noise. The**prediction**force model is based on predetermined ... autocorrelation function Rf and the actual ∆F measurements. Λ = z − λ , λ = 1,2,3... (17) That is: H(z) of force**prediction**yield the relation: A( z ) B( z ) (16) where ∆F is the force measured signal, and the filter output: ∧ R f ( z) = A+ The**prediction**error, E, is the difference between the filter desired responses: where F (t ) is the moving average 0 3 7

### DISIS: prediction of drug response through an iterative sure independence screening.

... the assumption that λ with the smallest

**prediction**error by cross validation is the optimal parameter. However, selecting parameter by cross validation is actually not a direct way to optimize the**prediction,**since the objective function of lasso is the regression fitting error with penalty function added, not the**prediction**error. So it is helpful to ... the subsequent model**prediction.**Moreover, these ‘redundant’ features may dominant the final feature list for having higher priority to be selected by SIS than other important features. In this work, we apply an iterative sure independence screening (ISIS) [11,12] to overcome the inherent disadvantages of SIS and further improve the**prediction**accuracy. ... explore the**prediction**performance for each drug of our method with respect to different top features selected by ISIS. Pearson correlation coefficients (PCCs) between real and predicted drug sensitivities (in terms of the activity area) for four example drugs are shown as PLOS ONE | DOI:10.1371/journal.pone.0120408 March 20, 2015 5 / 13 Prediction 0 3 13

### Academic locus of control and motivational persistence: structural equation modeling [Akademik kontrol odağı ve motivasyonel kararlılık: yapısal eşitlik modellemesi]

... structural

**equation**model. Structural**Equation**Modeling, or SEM, is a very general statistical modeling technique, which is widely used in the social sciences. It can be 82 Eğitim Bilimleri Araştırmaları Dergisi – Journal of Educational Sciences Research showed as a combination of factor analysis and regression or path analysis. Structural**equation**... Locus of Control and Motivational Persistence: Structural**Equation**Modeling Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. Testing structural**equation**models. (Eds: K. A. Bollen & J. S. Long Newbury Park). CA: Sage. pp. 136-162. Byrne, B. M. (2001). Structural**equation**modeling with AMOS: Basic concepts, applications and programming. ... control. There were also significant correlations between dimensions of motivational persistence. Structural**Equation**Model and Path Analysis This research was important in that it was tested through path analysis in the context of structural**equation**model, which was built up in the light of the literature. To this end, the model in figure 2 below was 0 3 14

### Existence of Three Solutions for $p$-biharmonic Equation

... Ricceri has been widely used to
solve diﬀerential equations, see [1, 2, 3, 5, 6, 7, 9, 12, 13] and reference therein.
The fourth-order

**equation**of nonlinearity furnishes a model to study traveling waves in suspension bridges; therefore this becomes very signiﬁcant in Physics. Many authors consider this type of**equation,**we refer to [4, 8, 10, 11] and there ... the p-biharmonic**equation,**will be established. The technical approach is mainly based on the three critical points theorem of B. Ricceri. Keywords : p-biharmonic, Navier condition; Multiple solutions, Three critical points theorem 1 Introduction and main results Consider the Navier boundary value problem involving the p-biharmonic**equation**{ ( ) −∆ ... u)ξ(x)dx, Ω 7 Journal of Nonlinear Analysis and Application ⟨J ′ (u), (ξ)⟩ = ∫ g(x, u)ξ(x)dx. Ω Hence, it follows from (1.2) that the weak solutions of**equation**(P) are exactly the solutions of the**equation**Φ′ (u) + λΨ′ (u) + µJ ′ (u) = 0. Due to (j3 ), for each λ > 0, it appears that lim (Φ(u) + λΨ(u)) = +∞, (2.8) ∥u∥→+∞ and therefore the ﬁrst assumption 0 3 9

### Stress and resilience in functional somatic syndromes--a structural equation modeling approach.

... Stress and Resilience in Functional Somatic
Syndromes – A Structural

**Equation**Modeling Approach Susanne Fischer1*, Gunnar Lemmer2, Mario Gollwitzer2, Urs M. Nater1 1 Clinical Biopsychology, Department of Psychology, University of Marburg, Marburg, ... somatic syndromes via chronic stress. We tested this model crosssectionally and prospectively. Methods: Young adults participated in a web survey at two time points. Structural**equation**modeling was used to test our model. The final sample consisted of 39054 participants, and 429 of these participated in the follow-up survey. Results: Our proposed model ... and treatment of functional somatic syndromes. Citation: Fischer S, Lemmer G, Gollwitzer M, Nater UM (2014) Stress and Resilience in Functional Somatic Syndromes – A Structural**Equation**Modeling Approach. PLoS ONE 9(11): e111214. doi:10.1371/journal.pone.0111214 Editor: Sharon Dekel, Harvard Medical School, United States of America Received March 29, 0 2 10

### Traveling wave solutions of a biological reaction-convection-diffusion equation model by using $(G'/G)$ expansion method

... class of nonlinear partial differential equations.
Keywords: Expansion methods, Reaction-convection-diffusion

**equation,**Nonlinear evolution equations, Exact Solutions 1 Introduction Mathematical modeling of physical and biological systems often leads to nonlinear evolution equations. Exact solutions of these equations are of theoretical importance. ... reaction-convection-diffusion**equation,**namely Murray**equation**which can be considered as a generalization of the Fisher and Burgers equations. The (G′ /G)-expansion method is based on the explicit linearization of nonlinear evolution equations for traveling waves with a certain substitution which leads to a second-order differential**equation**with constant ... reaction-convection-diffusion**equation**of the form ut = (λ + λ0 u)uxx + λ1 uux + λ2 u − λ3 u2 , (3.9) where λ , λ0 , λ1 , λ2 and λ3 are real constants [20]. In the particular case λ = 1 and λ0 = 0, this**equation**coincides with the Murray**equation**ut = uxx + λ1 uux + λ2 u − λ3 u2 , (3.10) which itself is a generalization of the well-known Fisher**equation**when λ1 0 2 5

### Design of a Prediction System for Hydrate Formation in Gas Pipelines using Wireless Sensor Network

... 9.
Fig. 9. NI Node 3202 consumed current at different link quality values
• Regression

**Equation**The regression**Equation**4 is developed based on the generated results in Table VII from the node power analysis. Microsoft Excel regression tool is used to develop the regression**equation**with a total number of 7 input data points. Where 𝐼 is the node consumed ... carried out on the measurement and**prediction**of hydrate formation temperature (HFT) for various gas mixtures. These studies can be classified as follows: a) Hand Calculation Methods K-VALUE METHOD The K-Value method was developed by “Wilcox et al” in 1941 [6], it utilizes the vapor-solid equilibrium constants for**prediction.**The hydrate forming conditions ... to the interpolations by developing an accurate three computerized**prediction**models based on the neural network algorithm to accurately predict the critical hydrate temperature at which hydration will form. The intellectual contribution in this phase is developing a new computerized**prediction**model that is based on the Well production quantities of 0 5 9

### Effects of Different Missing Data Imputation Techniques on the Performance of Undiagnosed Diabetes Risk Prediction Models in a Mixed-Ancestry Population of South Africa.

... the performance of undiagnosed
diabetes risk

**prediction**models during external validation. For this purpose, we use data for mixed-ancestry South African who took part in the Bellville-South study in Cape Town. PLOS ONE | DOI:10.1371/journal.pone.0139210 September 25, 2015 2 / 12 Data Imputation and**Prediction**Models Performance Material and Methods Database Details ... oral glucose tolerance test (OGTT) as prescribed by the WHO. Diabetes was diagnosed according to the WHO 2006 criteria [20]. Identification of undiagnosed diabetes**prediction**models Existing**prediction**models were obtained from a systematic review by Brown et al, 2012 [21]. Models met the criteria for model selection for this paper if they were developed ... diabetes from those at low risk PLOS ONE | DOI:10.1371/journal.pone.0139210 September 25, 2015 4 / 12 Data Imputation and**Prediction**Models Performance Table 1. Overview of the performance of the undiagnosed diabetes risk**prediction**models across the five imputation methods before (original) and after intercept adjustment (adjusted). Models Performance measure Deletion Simple Conditional Stochastic Multiple Original Adjusted Original Adjusted Original Adjusted Original Adjusted Original Adjusted 1.81 (1.09; 2.52) 1.22 (0.61; 1.83) 2.07 (1.40; 2.75) 1.28 (0.69– 1.87) 2.01 (1.28; 2.75) 1.27 (0.64– 1.90) 2.17 (1.41; 2.93) 1.27 (0.64– 1.90) 2.16 (1.40; 2.92) 1.30 (0.66– 1.94) Cambridge 0 2 12

### Recursive prediction error methods for online estimation in nonlinear state-space models

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### A Differential Evolution for Operon Prediction

... the urgency of developing an effective

**prediction**method. This research focuses on using machine learning and biological properties for operon**prediction.**Since the co-transcribed genes have the same biological properties, machine learning can be applied to these biological properties for operon**prediction.**The**prediction**results of an assay can be used ... distance on its own can yield good operon**prediction**results [2]. Since genes in the same operon often show similar functional relations, this property also provide good**prediction**results. Metabolic pathways [4], clusters of orthologous groups [5], and gene ontologies [3] are also often used for operon**prediction.**Operon**prediction**methods proposed in recent ... Although we only used three features for**prediction**(fewer than are used in other operon**prediction**methods), our method achieved a good balance between sensitivity and specificity. Since the resulting**prediction**accuracy compares well with that achieved by other methods, the proposed method can be used to solve operon**prediction**problems. TABLE V ACCURACY, 0 2 6

### Prediction of Skin Penetration using Artificial Neural Network

... empirical

**equation**of skin permeability as log Kp=0.71 log Kow -0.0061MW-6.3, where log Kp is skin permeability is given in cm/s, log Kow is solute partition coefficient in octanol/water, MW is molecular weight. Other researchers using other structural molecular parameters such as the number of hydrogen bonds and molecular volume 11 also proposed equations. ... are shown in Figure 1. The ANN model on the same training dataset produced results with R2=0.856 and MSE=0.04, respectively. There was very less error in**prediction.**This was trained network used for**prediction**of log kp value for other compounds. The ANN model can better predict skin permeability from Abraham descriptors (Table 5). Name QD-1 Logkp ... training subset and MSE was 0.04. In addition, the predictability of the neural network model was compared to the experimental data. This paper uses artificial neural network for**prediction**of Skin permeability study. Keywords: Artificial Neural Network, Mean Square Error, Correlation Coefficient, Abraham Parameters. 1. Introduction Transdermal therapeutic 0 2 6

### A coarse-grained elastic network atom contact model and its use in the simulation of protein dynamics and the prediction of the effect of mutations.

... (see methods). However we observe a trade-off between the

**prediction**of bfactors on one side and overlap and the effect of mutations on the other (see methods). Ultimately we opted for a parameter set that maximizes overlap and the**prediction**of mutations with complete disregard to b-factor predictions. Nonetheless, as shown below, even the lower correlations ... that existing methods are biased towards the**prediction**of destabilizing mutations and that ENCoM is less biased at predicting stabilizing mutations. Citation: Frappier V, Najmanovich RJ (2014) A Coarse-Grained Elastic Network Atom Contact Model and Its Use in the Simulation of Protein Dynamics and the**Prediction**of the Effect of Mutations. PLoS Comput ... of ENCoM in the**prediction**of the effect of mutations on protein stability. While existing methods are based on machine learning or enthalpic considerations, the use of ENCoM, based on vibrational normal modes, is based on entropic considerations. This represents a novel area of application for NMA methods and a novel approach for the**prediction**of the 0 2 21

### Numerical simulation of GEW equation using RBF collocation method

... width

**equation,**modiﬁed equal width**equation,**RBF collocation method 1 Introduction Various methods [1]-[2] have been devised to ﬁnd the exact and approximate solutions of nonlinear partial diﬀerential equations in order to provide more information for understanding many physical phenomena arising in numerous scientiﬁc and engineering ﬁelds. GEW**equation**... nonlinear wave**equation**of the form ut + ϵupux − µuxxt = 0, where p is a positive integer, ϵ and µ are positive constants which require the boundary conditions u → 0 as x → ±∞. It is related to the generalized regularized long wave (GRLW)**equation**[22, 23] and the generalized Korteweg-de Vries (GKdV)**equation**[17], based on the equal width (EW)**equation**... 00989131706401 1 pulse-like form. GEW**equation**was formulated by Peregrine [26, 27], and Benjamin et al. [4] as an alternative to GRLW**equation**and GKdV**equation**[22, 23, 5, 6] for studying soliton phenomena and as a model for small-amplitude long waves on the surface of water in a channel. The study of GEW**equation**provides the opportunity of investigating 0 8 28

### A HEURISTIC MOVING VEHICLE LOCATION PREDICTION TECHNIQUE VIA OPTIMAL PATHS SELECTION WITH AID OF GENETIC ALGORITHM AND FEED FORWARD BACK PROPAGATION NEURAL NETWORK

... Moving Vehicle Location

**Prediction**Technique Here, a method for predicting the vehicles future location is proposed. The proposed heuristic**prediction**technique consists of four stages namely, frequent paths selection, optimal paths**prediction**for each vehicle by GA, optimal paths training in FFBNN and vehicle future location**prediction**by FFBNN. This ... the result with the RBF classifier. The comparison result shows our proposed technique acquires an accurate vehicle location**prediction**ratio than the RBF**prediction**ratio, in terms of accuracy. Keywords: Moving Vehicle Location**Prediction,**Genetic Algorithm (GA), Feed Forward Back Propagation Neural Network (FFBNN), Frequent Paths, Radial Basis Function ... given in the following Table 3. The proposed heuristic technique**prediction**accuracy is shown in the below Table 4. The vehicle**prediction**accuracy is calculated by utilizing the formula. Accuracy = correctly predicted paths/total number optimal frequent paths. The proposed vehicle path**prediction**performance is compared with another one classifier 0 3 9

### Prediction of Participation of Undergraduate University Students in a Music and Dance Master’s Degree Program

... salience.Social Psychology Quarterly, 48: 203-215.
International Journal of Instruction, July 2015 ● Vol.8, No.2
174

**Prediction**of Participation of Undergraduate University … Charng, H. W., et al., (1988). Role identity and reasoned action in the**prediction**of repeated behavior.Social Psychology Quarterly, 51: 303-317. Chatzisarantis, N. L. D., &Hagger, ... of the theory of reasoned action and the theory of planned behavior in the**prediction**of condom use intentions in a national sample of english young people. Health psychology, 18(1): 72-81. Theodorakis, Y. (1994). Planned behavior, attitude strength, self-identity, and the**prediction**of exercise behavior.The Sport Psychologist, 8: 149-165. Theodorakis, ... excluding any motor skill acquisition. Research Aim Therefore, the study was conducted in an attempt: International Journal of Instruction, July 2015 ● Vol.8, No.2 168**Prediction**of Participation of Undergraduate University … (i) To investigate whether the application of the Theory ofPlanned Behavior can predict future intention specifically 0 3 12

### Improved Prediction of Preterm Delivery Using Empirical Mode Decomposition Analysis of Uterine Electromyography Signals.

... suitable for term and preterm delivery

**prediction.**A number of previous studies have used this TPEHG dataset to develop a reliable preterm delivery**prediction.**One such study generated moderate maximum and average Area Under the Curve PLOS ONE | DOI:10.1371/journal.pone.0132116 July 10, 2015 2 / 16 Preterm Delivery**Prediction**Using EMD Analysis of ... representative classifiers. Finally, three different electrode positions were analyzed for their**prediction**accuracy of preterm delivery in order to establish which uterine EMG recording location was optimal signal data. Overall, our results show a clear improvement in**prediction**accuracy of preterm delivery risk compared with previous approaches, achieving ... entropy ratios. In addition, six different classifiers were implemented in order to evaluate the**prediction**performance of preterm delivery. Finally, in order to evaluate whether a specific electrode position on the abdomen gave the best preterm delivery**prediction**accuracy, the uterine EMG data from three different positions were analyzed separately. Materials 0 4 16

### Empirical analysis of the International Classification of Functioning, Disability and Health (ICF) using structural equation modeling.

... this study had a sample size appropriate for a solid base for estimation using Structural

**Equation**Modeling. Analyses were performed with the Statistical Package for Social Sciences v.16 (SPSS Inc., Chicago, IL, USA) using the moment structure module. Structural**Equation**Modeling was developed using the Analysis of Moment Structures v.16 (SPSS Inc. AMOS). ... Disability Assessment Instrument II. The decimal numbers on the arrows indicate the significant correlation coefficients from the Structural**Equation**Modeling, showing positive and negative (-) associations.**Equation**Modeling. The results partially support some of the relationships in the ICF model, highlighting the essential influence of the contextual ... dependency of the functioning and disability processes, in addition to putting into perspective the impact of health conditions. Keywords: ICF; empirical analysis; structural**equation**model; rehabilitation. BULLET POINTS • Inter-relationships among functioning components had distinct magnitudes. • H ealth conditions had no direct effect on functioning 0 2 11

### Impact of oral health on physical and psychosocial dimensions: an analysis using structural equation modelingImpacto da saúde bucal nas dimensões física e psicossocial: uma análise através da modelagem com equações estruturais.

... física e psicossocial: uma análise através da modelagem com equações estruturais
Impact of oral health on the physical and psychosocial dimensions: an analysis using structural

**equation**modeling Impacto de la salud oral en las dimensiones física y psicosocial: un análisis de modelos de ecuaciones estructurales Marise Fagundes Silveira 1 João P. ... Health Impact Profile (OHIP-14). The covariates were: socioeconomic status, habits and health care, use of dental services, and normative conditions of oral health. Structural**equation**modeling was performed, and 15.6% of adolescents reported impact in at least one dimension of the OHIP-14. The dimensions that showed the highest prevalence of impact 0 3 15

### Artificial neural networks (ANN): prediction of sensory measurements from instrumental data.

... SCAMPICCHIO et al., 2006).
However, in order for instrumental measurements to replace sensory attributes, it is essential that they provide accurate predictions. This is achieved by first building a

**prediction**model, based on the calibration of the instrumental measurements with sensory measurements of the same objects (WILKINSON; YUKSEL, 1997). To ... nine sensory attributes of ten different types of meat broth assessed by a trained team. 3.2 Selecting the best architecture of the network**prediction**In order to determine the ANN configuration for the**prediction**of sensory measurements, the number of neurons in the first and second hidden layers varied from three to fifteen fixing the number of ... attributes of consistency and spreadability of light cheesecurds. Figure 2. Architecture of ANN selected for**prediction**of sensory attributes of consistency and spreadability. Food Sci. Technol, Campinas, 33(4): 722-729, Oct.-Dec. 2013 725 ANN’S:**prediction**of sensory measurements 3.3 Selection of the best number of iterations To get good performance 0 3 8

### Physical Stress Echocardiography: Prediction of Mortality and Cardiac Events in Patients with Exercise Test showing Ischemia.

... physical stress echocardiography in coronary artery disease. However, the

**prediction**of mortality and major cardiac events in patients with exercise test positive for myocardial ischemia is limited. Objective: To evaluate the effectiveness of physical stress echocardiography in the**prediction**of mortality and major cardiac events in patients with exercise ... Artigo Original Ecoestresse Físico: Predição de Mortalidade e Eventos Cardíacos em Pacientes com Ergometria Isquêmica Physical Stress Echocardiography:**Prediction**of Mortality and Cardiac Events in Patients with Exercise Test showing Ischemia Ana Carla Pereira de Araujo 1, Bruno F. de Oliveira Santos1, Flavia Ricci Calasans1, ... outcomes and cost-effectiveness. Minerva Cardioangiol. 2009;57(3):315-31. 32. Bouzas-Mosquera A, Peteiro J, Alvarez-Garcia N, Broullon FJ, Mosquera VX, Garcia-Bueno L, et al.**Prediction**of mortality and major cardiac events by exercise echocardiography in patients with normal exercise electrocardiographic testing. J Am Coll Cardiol. 2009;53(21):1981-90. 33. 0 2 8

### FACTORS AFFECTING PEAK EXPIRATORY FLOW RATE AND DER IVE PREDICTIVE EQUATION IN CHILDREN OF 6-12 YEARS OF AG E OF KARAIKAL

0 4 15

### Deterministic prediction of surface wind speed variations

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### Prediction of poor infant growth

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### Prediction of risk for drug use in high school students

... of
the relationship
of a given youngster
with his or her parents are similar to that
of a drug addict. This is a probability
statement, and although it is not a

**prediction**of a future fact, it is a**prediction**of a future risk. For those concerned with the possibility of “early tagging” it is important to keep in mind that the DRS does not ask questions ... differences in parent-child relations were found between these new groups. The DRS was also found to have reasonably high sensitivity and specificity. Its potential value as a risk**-prediction**instrument is discussed. S ociodemographic, personality, interpersonal, environmental, and other characteristics have been repeatedly identified as associated risk ... trying to change an attitude when a change in behavior is more appropriate). It is also important to emphasize that the identification of risk factors does not enable individual predictions to be made Bulletin of PAHO, 24(l), 1990 77 with great accuracy. Risk factors determine probability statements about groups of individuals of specific types that 0 1 9