Some non-linear height–diameter models performance for mixed stand in forests in Northwest Iran

This study evaluated the total height of trees based on diameter at breast height by using 23 widely used height-diameter non-linear regression models for mixed-species forest stands consisting of Caucasian oak,field maple,and hornbeam from forests in Northwest Iran.1920 trees were measured in 6 sampling plots(every sampling plot has 1 ha area).The fit of the best height–diameter models for each species were compared based on R2,Root Mean Square Error(RMSE),Akaike information criterion(AIC),standard error,and relative ranking performance criteria.In the final step,verification of results was performed by paired sample t-test to compare the observed height and estimated height.Results showed that among 23 height-diameter models,the best models were obtained from the top five ones including Modified-logistic,Prodan,Sibbesen,Burkhart,and Exponential.Comparison between the actual observed height and estimated height for Caucasian oak showed that Modified–Logistic,Prodan,Sibbesen,Burkhart,and Exponential performed better than the others,respectively(There were no statistically significant differences between observed heights and predicted height(p≥0.05)).Prodan,Modified-Logistic,Burkhart,and Loetch evaluated field maple tree height correctly,and Modified-Logistic,Burkhart,and Loetch had better fitness compared to the others for hornbeam,respectively.Although other models were introduced as appropriate criteria,they could not reliably predict the height of trees.Using the Rank analysis,the Modified-Logistic model for the Caucasian oak and Prodan model for field maple and hornbeam had the best performance.Finally,to complement the results of this study,it is suggested to assess how environmental factors such as elevation,climate parameters,forest protection policy and forest structure will modify height-diameter allometry models and will enhance the prediction accuracy of tree heights prediction in mixed stands.

Heavy metals adsorption by banana peels micro-powder: Equilibrium modeling by non-linear models

In this study the copper and lead adsorption efficiency onto banana peels powder was investigated. The agroindustrial waste recovery represents one of the Circular Economy pillars. In the view of the synthesis of an environmentally friendly adsorbent material, the powder was used without any preliminary chemical or thermal activation, but only after simple washing, drying and grinding. The bio-adsorbent was characterized by the FTIR technique and tested in batch mode on synthetic aqueous solutions containing Pb and Cu in the range 10–90 mg·L^(-1). A selection of two(Langmuir, Freundlich) and three(Sips, Redlich–Peterson, Koble–Corrigan) parameter isotherm models was chosen to fit adsorption equilibrium data by non-linear regression procedure. The best fit isotherm model was selected relying on the error function with the lowest average percentage error(APE) value, among those characterized by the highest R^2 values. As expected, the three-parameter models are found to better represent both metals bio-adsorption, with APE and R^2 values always lower and higher, respectively, than the corresponding values obtained for the two-parameter models.

Non-Linear Mathematical Model of the Interaction between Tumor and Oncolytic Viruses

A mathematical modeling of tumor therapy with oncolytic viruses is discussed. The model consists of two coupled, deterministic differential equations allowing for cell reproduction and death, and cell infection. The model is one of the conceptual mathematical models of tumor growth that treat a tumor as a dynamic society of interacting cells. In this paper, we obtain an approximate analytical expression of uninfected and infected cell population by solving the non-linear equations using Homotopy analysis method (HAM). Furthermore, the results are compared with the numerical simulation of the problem using Matlab program. The obtained results are valid for the whole solution domain.

A Statistical Model with Non-Linear Effects and Non-Proportional Hazards for Breast Cancer Survival Analysis

The Cox proportional hazard model is being used extensively in oncology in studying the relationship between survival times and prognostic factors. The main question that needs to be addressed with respect to the applicability of the Cox PH model is whether the proportional hazard assumption is met. Failure to justify the subject assumption will lead to misleading results. In addition, identifying the correct functional form of the continuous covariates is an important aspect in the development of a Cox proportional hazard model. The purpose of this study is to develop an extended Cox regression model for breast cancer survival data which takes non-proportional hazards and non-linear effects that exist in prognostic factors into consideration. Non-proportional hazards and non-linear effects are detected using methods based on residuals. An extended Cox model with non-linear effects and time-varying effects is proposed to adjust the Cox proportional hazard model. Age and tumor size were found to have nonlinear effects. Progesterone receptor assay status and age violated the proportional hazard assumption in the Cox model. Quadratic effect of age and progesterone receptor assay status had hazard ratio that changes with time. We have introduced a statistical model to overcome the presence of the proportional hazard assumption violation for the Cox proportional hazard model for breast cancer data. The proposed extended model considers the time varying nature of the hazard ratio and non-linear effects of the covariates. Our improved Cox model gives a better insight on the hazard rates associated with the breast cancer risk factors.

Load Frequency Control of Small Hydropower Plants Using One-Input Fuzzy PI Controller with Linear and Non-Linear Plant Model

This study presents an intelligent approach for load frequency control (LFC) of small hydropower plants (SHPs). The approach which is based on fuzzy logic (FL), takes into account the non-linearity of SHPs—something which is not possible using traditional controllers. Most intelligent methods use two- input fuzzy controllers, but because such controllers are expensive, there is economic interest in the relatively cheaper single-input controllers. A non- linear control model based on one-input fuzzy logic PI (FLPI) controller was developed and applied to control the non-linear SHP. Using MATLAB/Si- mulink SimScape, the SHP was simulated with linear and non-linear plant models. The performance of the FLPI controller was investigated and compared with that of the conventional PI/PID controller. Results show that the settling time for the FLPI controller is about 8 times shorter;while the overshoot is about 15 times smaller compared to the conventional PI/PID controller. Therefore, the FLPI controller performs better than the conventional PI/PID controller not only in meeting the LFC control objective but also in ensuring increased dynamic stability of SHPs.

Processing Approach of Non-linear Adjustment Models in the Space of Non-linear Models

This paper investigates the mathematic features of non-linear models and discusses the processing way of non-linear factors which contributes to the non-linearity of a non-linear model. On the basis of the error definition,this paper puts forward a new adjustment criterion, SGPE.Last,this paper investigates the solution of a non-linear regression model in the non-linear model space and makes the comparison between the estimated values in non-linear model space and those in linear model space.

Stochastic Models to Mitigate Sparse Sensor Attacks in Continuous-Time Non-Linear Cyber-Physical Systems

Cyber-Physical Systems are very vulnerable to sparse sensor attacks.But current protection mechanisms employ linear and deterministic models which cannot detect attacks precisely.Therefore,in this paper,we propose a new non-linear generalized model to describe Cyber-Physical Systems.This model includes unknown multivariable discrete and continuous-time functions and different multiplicative noises to represent the evolution of physical processes and randomeffects in the physical and computationalworlds.Besides,the digitalization stage in hardware devices is represented too.Attackers and most critical sparse sensor attacks are described through a stochastic process.The reconstruction and protectionmechanisms are based on aweighted stochasticmodel.Error probability in data samples is estimated through different indicators commonly employed in non-linear dynamics(such as the Fourier transform,first-return maps,or the probability density function).A decision algorithm calculates the final reconstructed value considering the previous error probability.An experimental validation based on simulation tools and real deployments is also carried out.Both,the new technology performance and scalability are studied.Results prove that the proposed solution protects Cyber-Physical Systems against up to 92%of attacks and perturbations,with a computational delay below 2.5 s.The proposed model shows a linear complexity,as recursive or iterative structures are not employed,just algebraic and probabilistic functions.In conclusion,the new model and reconstructionmechanism can protect successfully Cyber-Physical Systems against sparse sensor attacks,even in dense or pervasive deployments and scenarios.

Power Allocation for Wireless Powered MIMO Transmissions with Non-Linear RF Energy Conversion Models

We study a radio frequency(RF) wireless energy transfer(WET) enabled multiple input multiple output(MIMO) system. A time slotted transmission pattern is considered. Each slot can be divided into two phases, downlink(DL) WET and uplink(UL) wireless information transmission(WIT). Since energy conversion efficiency of the energy harvesting circuits are non.linear, the conventional linear model leads to a mismatch for resource allocation. In this paper, the power allocation algorithm considering the practical non.linear energy harvesting circuits is studied. The optimization problem is formulated to maximize the energy efficiency of system with multiple constraints, i.e., the transmission power, the received power and the minimum harvested energy, which is a non.convex problem. We transform the objective function from fractional form into an equivalent objective function in subtractive form and provide an iterative power allocation algorithm to achieve the optimal solution. Numerical results show that our proposed algorithm with the non.linear RF energy conversion models can achieve much better performance than the algorithm with the conventional linear model.

NON-LINEAR DYNAMIC MODEL RETRIEVAL OF SUBTROPICAL HIGH BASED ON EMPIRICAL ORTHOGONAL FUNCTION AND GENETIC ALGORITHM

Aiming at the difficulty of accurately constructing the dynamic model of subtropical high, based on the potential height field time series over 500 hPa layer of T106 numerical forecast products, by using EOF（empirical orthogonal function） temporal-spatial separation technique, the disassembled EOF time coefficients series were regarded as dynamical model variables, and dynamic system retrieval idea as well as genetic algorithm were introduced to make dynamical model parameters optimization search, then, a reasonable non-linear dynamic model of EOF time-coefficients was established. By dynamic model integral and EOF temporal-spatial components assembly, a mid-/long-term forecast of subtropical high was carried out. The experimental results show that the forecast results of dynamic model are superior to that of general numerical model forecast results. A new modeling idea and forecast technique is presented for diagnosing and forecasting such complicated weathers as subtropical high.

ON USING NON-LINEAR CANONICAL CORRELATION ANALYSIS FOR VOICE CONVERSION BASED ON GAUSSIAN MIXTURE MODEL

Voice conversion algorithm aims to provide high level of similarity to the target voice with an acceptable level of quality.The main object of this paper was to build a nonlinear relationship between the parameters for the acoustical features of source and target speaker using Non-Linear Canonical Correlation Analysis(NLCCA) based on jointed Gaussian mixture model.Speaker indi-viduality transformation was achieved mainly by altering vocal tract characteristics represented by Line Spectral Frequencies(LSF).To obtain the transformed speech which sounded more like the target voices,prosody modification is involved through residual prediction.Both objective and subjective evaluations were conducted.The experimental results demonstrated that our proposed algorithm was effective and outperformed the conventional conversion method utilized by the Minimum Mean Square Error(MMSE) estimation.

Adsorption of Naphthol Green B on unburned carbon：2-and3-parameter linear and non-linear equilibrium modelling

The study deals with adsorption of Naphthol Green B on two unburned carbons and the parent coal,from which the UCs have been created in a fluidised-bed power station.Particular attention has been paid to the adsorption equilibrium modelling:experimental data has been analysed using 2-parameter(Langmuir,Freundlich) and3-parameter(Redlich-Peterson) isotherms — both linear and non-linear regressions have been used for the estimation of the isotherm parameters.In the case of both UCs,the Langmuir isotherm model provides the worst fit,whereas 2-parameter Freundlich and 3-parameter Redlich-Peterson models are both good,from which 3-parameter Redlich-Peterson isotherm provides slightly better results(despite the penalty used for the higher number of parameters).In the case of both UCs,the linear regression of Freundlich and Redlich-Peterson models provides good results(comparable with non-linear regressions).Unlike both UCs,the best fit of the experimental data from the adsorption on the coal has been achieved by the Langmuir isotherm model.The results based on the Freundlich or Redlich-Peterson model were(in this case) somewhat worse.

Assessing the effects of model parameter assumptions on surface-wave inversion results

Surface-wave inversion is a powerful tool for revealing the Earth's internal structure.However,aside from shear-wave velocity(v_(S)),other parameters can influence the inversion outcomes,yet these have not been systematically discussed.This study investigates the influence of various parameter assumptions on the results of surface-wave inversion,including the compressional and shear velocity ratio(v_(P)/v_(S)),shear-wave attenuation(Q_(S)),density(ρ),Moho interface,and sedimentary layer.We constructed synthetic models to generate dispersion data and compared the obtained results with different parameter assumptions with those of the true model.The results indicate that the v_(P)/v_(S) ratio,Q_(S),and density(ρ) have minimal effects on absolute velocity values and perturbation patterns in the inversion.Conversely,assumptions about the Moho interface and sedimentary layer significantly influenced absolute velocity values and perturbation patterns.Introducing an erroneous Mohointerface depth in the initial model of the inversion significantly affected the v_(S) model near that depth,while using a smooth initial model results in relatively minor deviations.The assumption on the sedimentary layer not only affects shallow structure results but also impacts the result at greater depths.Non-linear inversion methods outperform linear inversion methods,particularly for the assumptions of the Moho interface and sedimentary layer.Joint inversion with other data types,such as receiver functions or Rayleigh wave ellipticity,and using data from a broader period range or higher-mode surface waves,can mitigate these deviations.Furthermore,incorporating more accurate prior information can improve inversion results.

Influence of Confined Concrete Models on the Seismic Response of RC Frames

In this study, the influence of confined concrete models on the response of reinforced concrete structures is investigatedat member and global system levels. The commonly encountered concrete models such as Modified Kent-Park, Saatçioğlu-Razvi, and Mander are considered. Two moment-resisting frames designed according to thepre-modern code are taken into consideration to reflect the example of an RC moment-resisting frame in thecurrent building stock. The building is in an earthquake-prone zone located on Z3 Soil Type. The inelasticresponse of the building frame is modelled by considering the plastic hinges formed on each beam and columnelement for different concrete classes and stirrups spacings. The models are subjected to non-linear static analyses.The differences between confined concrete models are comparatively investigated at both reinforced concretemember and system levels. Based on the results of the comparative analysis, it is revealed that the column behaviouris mostly influenced by the choice of model, due to axial loads and confinement effects, while the beams areless affected, and also it is observed that the differences exhibited in the moment-curvature response of columncross-sections do not significantly affect the overall behaviour of the global system. This highlights the critical roleof model selection relative to the concrete strength and stirrup spacing of the member.

THE MODEL VALIDATION OF DYNAMIC NEURAL NETWORKS

This paper investigates the problem of the model validation in identifying discrete-time-nonlinear dynamic systems by using neural networks with a single hidden layer.Based on the estimation theory,a synthetic error-index(SEI)criterion for the neural network models has been developed.By using the powerful training algorithm of recursive prediction error (RPE),two simulated non-linear systems are studied,and the results show that the synthetic error-index criterion can be used to verify the dynamic neural network models.Furthermore,the proposed technique is much simple in calculation than that of the effective correlation tests.Finally,some problems required by further study are discussed.

Comparative efficiency analysis of different nonlinear modelling strategies to simulate the biaxial response of RC columns

The performance of different nonlinear modelling strategies to simulate the response of RC columns subjected to axial load combined with cyclic biaxial horizontal loading is compared. The models studied are classified into two categories according to the nonlinearity distribution assumed in the elements： lumped-plasticity and distributed inelasticity. For this study, results of tests on 24 columns subjected to cyclic uniaxial and biaxial lateral displacements were numerically reproduced. The analyses show that the global envelope response is satisfactorily represented with the three modelling strategies, but significant differences were found in the strength degradation for higher drift demands and energy dissipation.

An analytical analysis for the mechanical performance of fully-grouted rockbolts based on the exponential softening model

This paper presented a novel bond-slip model to better reveal the mechanical behavior of the bolt-grout interface for fully-grouted rockbolts under tensile loads by considering the non-linear response in the softening stage.The exponential decay function is adopted for describing the non-linear response in the softening stage.Based on the improved bond-slip model,the corresponding analytical solutions for the interfacial shear stress and the axial force of the bolt under different loading stages are solved.Then,the validity of this proposed model was verified by comparing with the experimental results.The results show that compared with the linear softening model,the proposed model is more suitable for predicting the mechanical performance of fully-grouted rockbolts.Finally,a series of parametric studies are conducted to explore the effect of model parameters on the mechanical properties of fully-grouted rockbolts.The results indicate that compared with the anchor length,the bolt diameter and the bond strength of the bolt-grout interface have a significance influence on the ultimate load of bolt,especially for the elastic and softening stage.Moreover,it can be found that using the linear softening model maybe overestimates the supporting performance of grouted bolt,resulting in an unsafe design for bolt.

The application of modeling and prediction with MRA wavelet network

As there are lots of non-linear systems in the real engineering, it is very important to do more researches on the modeling and prediction of non-linear systems. Based on the multi-resolution analysis (MRA) of wavelet theory, this paper combined the wavelet theory with neural network and established a MRA wavelet network with the scaling function and wavelet function as its neurons. From the analysis in the frequency domain, the results indicated that MRA wavelet network was better than other wavelet networks in the ability of approaching to the signals. An essential research was can:led out on modeling and prediction with MRA wavelet network in the non-linear system. Using the lengthwise sway data received from the experiment of ship model, a model of offline prediction was established and was applied to the short-time prediction of ship motion. The simulation results indicated that the forecasting model improved the prediction precision effectively, lengthened the forecasting time and had a better prediction results than that of AR linear model. The research indicates that it is feasible to use the MRA wavelet network in the short-time prediction of ship motion.

On the Non-linear Motion of IGS Station

With the daily SINEX files of the IGS, the time series of IGS stations are obtained using an independently developed software under generalized network adjustment models with coordinate patterns. From the time series, non-linear motions are found. With spectral analysis method, the variation frequency （annual period and semi-annual period） of the site velocity is found. Moreover, the empirical model of the velocity variation of the station has been established by regression analysis method based on the weekly solution coordinate series of the station. With respect to the velocity of the IGS tracking station, it was better to model the variation periodically or to give a velocity periodically using a piece-wise linear function rather than a linear variable to estimate its bias.

A Hybrid Neural Network and Box-Jenkins Models for Time Series Forecasting

Time series forecasting plays a significant role in numerous applications,including but not limited to,industrial planning,water consumption,medical domains,exchange rates and consumer price index.The main problem is insufficient forecasting accuracy.The present study proposes a hybrid forecastingmethods to address this need.The proposed method includes three models.The first model is based on the autoregressive integrated moving average(ARIMA)statistical model;the second model is a back propagation neural network(BPNN)with adaptive slope and momentum parameters;and the thirdmodel is a hybridization between ARIMA and BPNN(ARIMA/BPNN)and artificial neural networks and ARIMA(ARIMA/ANN)to gain the benefits of linear and nonlinearmodeling.The forecasting models proposed in this study are used to predict the indices of the consumer price index(CPI),and predict the expected number of cancer patients in the Ibb Province in Yemen.Statistical standard measures used to evaluate the proposed method include(i)mean square error,(ii)mean absolute error,(iii)root mean square error,and(iv)mean absolute percentage error.Based on the computational results,the improvement rate of forecasting the CPI dataset was 5%,71%,and 4%for ARIMA/BPNN model,ARIMA/ANN model,and BPNN model respectively;while the result for cancer patients’dataset was 7%,200%,and 19%for ARIMA/BPNNmodel,ARIMA/ANN model,and BPNNmodel respectively.Therefore,it is obvious that the proposed method reduced the randomness degree,and the alterations affected the time series with data non-linearity.The ARIMA/ANN model outperformed each of its components when it was applied separately in terms of increasing the accuracy of forecasting and decreasing the overall errors of forecasting.

Quadratic and cubic eddy-viscosity models in turbulent supercavitating flow computation

Quadratic and cubic non-linear eddy-viscosity turbulence models(NLEVM) with low Reynolds number(Re) correction were presented to provide better description of anisotropic turbulence stresses in the numerical prediction of supercavitating flows,which are accompanied with large density ratio and large-scaled swirling flow structures.The applications of the NLEVM were carried out through a self-developed cavitation codes,coupled with a cavitation model based on the transport equation of liquid phase.These NLEVM were verified capable of capturing more accurate macroscopic shape and hydrodynamic property of supercavity by the benchmark problems of supercavities over simple objects.Finally,the cubic NLEVM was further applied to the numerical prediction of supercavitating flow around a complex submerged vehicle.The corresponding cavitation behaviors were explored in detail to provide beneficial experience for further research.