The functions of dams are for water supply, flood control and hydroelectric power generation. A concrete faced rockfill dam (CFRD) is preferred by dam consultants due to its many advantages. It is designed to withstand all applied loads, namely gravity load due to its massive weight and hydrostatic load due to water thrust from the reservoir. The structural response of the Bakun CFRD which is the second highest dam in the world after the Shuibuya Dam, was analyzed using finite element method. A two-dimensional plane strain finite element analysis of the non-linear Duncan-Chang hyperbolic model was used in the study of the dam in respect to the deformation and stresses of the main dam of Bakun CFRD project. The Dead-Birth-Ghost element technique was used to simulate sequences of the construction of the dam. The comparison of rigid and flexible foundation on the behaviour of the dam is discussed in this study. In the finite element modeling, the concrete slab on the upstream was represented through a six-noded element, while the interface characteristic between the dam body and the concrete slab was modeled using an interface element. The maximum settlement and stresses of the cross-section were found and their distribution was discussed and tabulated in the form of contours. The effect of reservoir filling loading had a gradual effect on the dam response behaviour. Comparisons with no water impoundment in the dam were also discussed.
In this manuscript, we investigate a nonlinear delayed model to study the dynamics of human-immunodeficiency-virus in the population. For analysis, we find the equilibria of a susceptible-infectious-immune system with a delay term. The well-established tools such as the Routh-Hurwitz criterion, Volterra-Lyapunov function, and Lasalle invariance principle are presented to investigate the stability of the model. The reproduction number and sensitivity of parameters are investigated. If the delay tactics are decreased, then the disease is endemic. On the other hand, if the delay tactics are increased then the disease is controlled in the population. The effect of the delay tactics with subpopulations is investigated. More precisely, all parameters are dependent on delay terms. In the end, to give the strength to a theoretical analysis of the model, a computer simulation is presented.
Minimal available information concerning hip morphology is the motivation for several researchers to study the difference between Asian and Western populations. Current use of a universal hip stem of variable size is not the best option for all femur types. This present study proposed a new design process of the cementless femoral stem using a three dimensional model which provided more information and accurate analysis compared to conventional methods.
The present paper discusses the dynamics and optimal harvesting of an intraguild prey-predator fishery model by incorporating the nonlinear Michaelis-Menten type of harvesting in predator. To our knowledge, there is limited literature working on Michaelis-Menten type of harvesting in a three species intraguild model with variable carrying capacity. The carrying capacity is proportional to the density of biotic resource. The existence of the possible equilibria has been studied along with the stability criteria. We consider the impact of predator fish harvesting as the bifurcation parameter to analyze the long time behavior of the proposed system. From the economic perspective, bionomic equilibrium of the system is studied and optimal harvesting policy is derived with the assistance of Pontryagin Maximum Principle. Finally, numerical results are presented to verify our analytical results. It is shown that in the bifurcation diagrams, the system can exhibit transcritical and Hopf bifurcations in the neighborhood of coexistence equilibrium at low and relatively high level of predator harvesting respectively. Interestingly, the system enters to a bistable region where both the coexistence and predator-free equilibria can be stable depending on the initial values. This bistable behavior might be novel to the existing literature that studied intraguild models with variable carrying capacity. The objective of this study is to derive the optimal threshold for the predator harvesting that gives maximum financial profit while sustaining the fishery resources.
Identifying the emotional state is helpful in applications involving patients with autism and other intellectual disabilities; computer-based training, human computer interaction etc. Electrocardiogram (ECG) signals, being an activity of the autonomous nervous system (ANS), reflect the underlying true emotional state of a person. However, the performance of various methods developed so far lacks accuracy, and more robust methods need to be developed to identify the emotional pattern associated with ECG signals.
Effective fixation of fracture requires careful selection of a suitable implant to provide stability and durability. Implant with a feature of locking plate (LP) has been used widely for treating distal fractures in femur because of its favourable clinical outcome, but its potential in fixing proximal fractures in the subtrochancteric region has yet to be explored. Therefore, this comparative study was undertaken to demonstrate the merits of the LP implant in treating the subtrochancteric fracture by comparing its performance limits against those obtained with the more traditional implants; angle blade plate (ABP) and dynamic condylar screw plate (DCSP).
Cardiotocography (CTG) is the most widely used tool for fetal surveillance. The visual analysis of fetal heart rate (FHR) traces largely depends on the expertise and experience of the clinician involved. Several approaches have been proposed for the effective interpretation of FHR. In this paper, a new approach for FHR feature extraction based on empirical mode decomposition (EMD) is proposed, which was used along with support vector machine (SVM) for the classification of FHR recordings as 'normal' or 'at risk'.
Biosorption potential of mustard oil cake (MOC) for Ni(II) from aqueous medium was studied. Spectroscopic studies showed possible involvement of acidic (hydroxyl, carbonyl and carboxyl) groups in biosorption. Optimum biosorption was observed at pH 8. Contact time, reaction temperature, biosorbent dose and adsorbate concentration showed significant influence. Linear and non-linear isotherms comparison suggests applicability of Temkin model at 303 and 313 K and Freundlich model at 323K. Kinetics studies revealed applicability of Pseudo-second-order model. The process was endothermic and spontaneous. Freundlich constant (n) and activation energy (Ea) values confirm physical nature of the process. The breakthrough and exhaustive capacities for 5 mg/L initial Ni(II) concentration were 0.25 and 4.5 mg/g, while for 10 mg/L initial Ni(II) concentration were 4.5 and 9.5 mg/g, respectively. Batch desorption studies showed maximum Ni(II) recovery in acidic medium. Regeneration studies by batch and column process confirmed reutilization of biomass without appreciable loss in biosorption.
We study generalized variable projective synchronization between two unified time delayed systems with constant and modulated time delays. A novel Krasovskii-Lyapunov functional is constructed and a generalized sufficient condition for synchronization is derived analytically using the Lyapunov stability theory and adaptive techniques. The proposed scheme is valid for a system of n-numbers of first order delay differential equations. Finally, a new neural oscillator is considered as a numerical example to show the effectiveness of the proposed scheme.
The dynamics of a periodically delta-kicked Hamiltonian system moving at low speed (i.e., at speed much less than the speed of light) is studied numerically. In particular, the trajectory of the system predicted by Newtonian mechanics is compared with the trajectory predicted by special relativistic mechanics for the same parameters and initial conditions. We find that the Newtonian trajectory, although close to the relativistic trajectory for some time, eventually disagrees completely with the relativistic trajectory, regardless of the nature (chaotic, nonchaotic) of each trajectory. However, the agreement breaks down very fast if either the Newtonian or relativistic trajectory is chaotic, but very much slower if both the Newtonian and relativistic trajectories are nonchaotic. In the former chaotic case, the difference between the Newtonian and relativistic values for both position and momentum grows, on average, exponentially. In the latter nonchaotic case, the difference grows much slower, for example, linearly on average.
In this paper, we investigate the dynamical behavior in an M-dimensional nonlinear hyperchaotic model (M-NHM), where the occurrence of multistability can be observed. Four types of coexisting attractors including single limit cycle, cluster of limit cycles, single hyperchaotic attractor, and cluster of hyperchaotic attractors can be found, which are unusual behaviors in discrete chaotic systems. Furthermore, the coexistence of asymmetric and symmetric properties can be distinguished for a given set of parameters. In the endeavor of chaotification, this work introduces a simple controller on the M-NHM, which can add one more loop in each iteration, to overcome the chaos degradation in the multistability regions.
Ionic liquids (ILs) are class of solvent whose properties can be modified and tuned to meet industrial requirements. However, a high number of potentially available cations and anions leads to an even increasing members of newly-synthesized ionic liquids, adding to the complexity of understanding on their impact on aquatic organisms. Quantitative structure activity∖property relationship (QSAR∖QSPR) technique has been proven to be a useful method for toxicity prediction. In this work,σ-profile descriptors were used to build linear and non-linear QSAR models to predict the ecotoxicities of a wide variety of ILs towards bioluminescent bacterium Vibrio fischeri. Linear model was constructed using five descriptors resulting in high accuracy prediction of 0.906. The model performance and stability were ascertained using k-fold cross validation method. The selected descriptors set from the linear model was then used in multilayer perceptron (MLP) technique to develop the non-linear model, the accuracy of the model was further enhanced achieving high correlation coefficient with the lowest value being 0.961 with the highest mean square error of 0.157.
Low temperature thermal pre-treatment is a low-cost method to break down the structure of extracellular polymeric substances in waste activated sludge (WAS) while improving the sludge biodegradability. However, previous models on low temperature thermal pre-treatment did not adequately elucidate the behaviour of sludge hydrolysis process for the duration ranging from 5 to 9 h. Therefore, this work had developed an inclusive functional model to describe the kinetics of sludge hydrolysis for a wide range of treatment conditions (30 °C-90 °C within 0 and 16 h). As compared with treatment duration, the treatment temperature played a greater impact in solubilizing WAS. Accordingly, the 90 °C treatment had consistently produced WAS with the highest degree of solubility. Nonetheless, the mediocre discrepancies between 90 °C and 75 °C may challenge the practicality of increasing the treatment temperatures beyond 75 °C. The effects of treatment duration on soluble chemical oxygen demand, soluble carbohydrate and soluble protein were only significant during the first 4 h, except for humic substances release that continued to increase with treatment duration. Finally, a good fit with R2 > 0.95 was achieved using an inclusive multivariate non-linear model, substantiating the functionality to predict the kinetics of sludge hydrolysis at arbitrary treatment conditions.
PURPOSE: Non-linear regression analysis was used to determine dark adaptation indices in people with retinitis pigmentosa and in control subjects.
METHODS: Dark adaptation data were collected for 13 people with retinitis pigmentosa and 21 controls using the Goldmann-Weekers Dark Adaptometer. Data were analysed using an exponential non-linear regression model and dark adaptation indices derived. The results were compared to age-related values.
RESULTS: The mean cone threshold of the group with RP (4.73 +/- 0.19 log units) was significantly greater than that found in the control group (3.69 +/- 0.12 log units). The rate of cone dark adaptation in the RP group was not significantly different from that of the control group. The a break in the RP group (6.46 +/- 0.70 minutes) was delayed when compared to the control group (4.29 +/- 0.21 minutes) and the rate of rod dark adaptation in the RP group was slower (10 +/- 2 per cent per minute) than that of the control group (15 +/- 1 per cent per minute).
CONCLUSIONS: This study has shown that a relatively simple data analysis can provide a more quantitative and intuitive description of dark adaptation rates in people with retinal disease. This technique will enable more effective use of dark adaptometry as a supplement to objective electrophysiology, when monitoring people with retinitis pigmentosa.
Study site: Retinitis Pigmentosa Society of NSW, the National Foundation of Blind Citizens in New South Wales and the Low Vision Clinic of the School of Optometry University of NSW, Australia
Metformin is contraindicated in patients with renal impairment; however, there is poor adherence to current dosing guidelines. In addition, the pharmacokinetics of metformin in patients with significant renal impairment are not well described. The aims of this study were to investigate factors influencing the pharmacokinetic variability, including variant transporters, between healthy subjects and patients with type 2 diabetes mellitus (T2DM) and to simulate doses of metformin at varying stages of renal function.
Researchers used a hybrid model (a combination of health resource demand model and disease transmission model), Bayesian model, and susceptible-exposed-infectious-removed (SEIR) model to predict health service utilization and deaths and mixed-effect nonlinear regression. Further, they used the mixture model to predict the number of confirmed cases and deaths or to predict when the curve would flatten. In this article, we show, through scenarios developed using system dynamics methodology, besides close to real-world results, the detrimental effects of ignoring social distancing guidelines (in terms of the number of people infected, which decreased as the percentage of noncompliance decreased).
Brain magnetic resonance imaging (MRI) classification into normal and abnormal is a critical and challenging task. Owing to that, several medical imaging classification techniques have been devised in which Learning Vector Quantization (LVQ) is amongst the potential. The main goal of this paper is to enhance the performance of LVQ technique in order to gain higher accuracy detection for brain tumor in MRIs. The classical way of selecting the winner code vector in LVQ is to measure the distance between the input vector and the codebook vectors using Euclidean distance function. In order to improve the winner selection technique, round off function is employed along with the Euclidean distance function. Moreover, in competitive learning classifiers, the fitting model is highly dependent on the class distribution. Therefore this paper proposed a multiresampling technique for which better class distribution can be achieved. This multiresampling is executed by using random selection via preclassification. The test data sample used are the brain tumor magnetic resonance images collected from Universiti Kebangsaan Malaysia Medical Center and UCI benchmark data sets. Comparative studies showed that the proposed methods with promising results are LVQ1, Multipass LVQ, Hierarchical LVQ, Multilayer Perceptron, and Radial Basis Function.
The purpose of this research is to analyse the relationship between nonlinear dynamic character and individuals' standing balance by the largest Lyapunov exponent, which is regarded as a metric for assessing standing balance. According to previous study, the largest Lyapunov exponent from centre of pressure time series could not well quantify the human balance ability. In this research, two improvements were made. Firstly, an external stimulus was applied to feet in the form of continuous horizontal sinusoidal motion by a moving platform. Secondly, a multiaccelerometer subsystem was adopted. Twenty healthy volunteers participated in this experiment. A new metric, coordinated largest Lyapunov exponent was proposed, which reflected the relationship of body segments by integrating multidimensional largest Lyapunov exponent values. By using this metric in actual standing performance under sinusoidal stimulus, an obvious relationship between the new metric and the actual balance ability was found in the majority of the subjects. These results show that the sinusoidal stimulus can make human balance characteristics more obvious, which is beneficial to assess balance, and balance is determined by the ability of coordinating all body segments.
Coronary artery disease (CAD) is one of the dangerous cardiac disease, often may lead to sudden cardiac death. It is difficult to diagnose CAD by manual inspection of electrocardiogram (ECG) signals. To automate this detection task, in this study, we extracted the heart rate (HR) from the ECG signals and used them as base signal for further analysis. We then analyzed the HR signals of both normal and CAD subjects using (i) time domain, (ii) frequency domain and (iii) nonlinear techniques. The following are the nonlinear methods that were used in this work: Poincare plots, Recurrence Quantification Analysis (RQA) parameters, Shannon entropy, Approximate Entropy (ApEn), Sample Entropy (SampEn), Higher Order Spectra (HOS) methods, Detrended Fluctuation Analysis (DFA), Empirical Mode Decomposition (EMD), Cumulants, and Correlation Dimension. As a result of the analysis, we present unique recurrence, Poincare and HOS plots for normal and CAD subjects. We have also observed significant variations in the range of these features with respect to normal and CAD classes, and have presented the same in this paper. We found that the RQA parameters were higher for CAD subjects indicating more rhythm. Since the activity of CAD subjects is less, similar signal patterns repeat more frequently compared to the normal subjects. The entropy based parameters, ApEn and SampEn, are lower for CAD subjects indicating lower entropy (less activity due to impairment) for CAD. Almost all HOS parameters showed higher values for the CAD group, indicating the presence of higher frequency content in the CAD signals. Thus, our study provides a deep insight into how such nonlinear features could be exploited to effectively and reliably detect the presence of CAD.
Cardiovascular diseases (CVDs) are the leading cause of deaths worldwide. The rising mortality rate can be reduced by early detection and treatment interventions. Clinically, electrocardiogram (ECG) signal provides useful information about the cardiac abnormalities and hence employed as a diagnostic modality for the detection of various CVDs. However, subtle changes in these time series indicate a particular disease. Therefore, it may be monotonous, time-consuming and stressful to inspect these ECG beats manually. In order to overcome this limitation of manual ECG signal analysis, this paper uses a novel discrete wavelet transform (DWT) method combined with nonlinear features for automated characterization of CVDs. ECG signals of normal, and dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM) and myocardial infarction (MI) are subjected to five levels of DWT. Relative wavelet of four nonlinear features such as fuzzy entropy, sample entropy, fractal dimension and signal energy are extracted from the DWT coefficients. These features are fed to sequential forward selection (SFS) technique and then ranked using ReliefF method. Our proposed methodology achieved maximum classification accuracy (acc) of 99.27%, sensitivity (sen) of 99.74%, and specificity (spec) of 98.08% with K-nearest neighbor (kNN) classifier using 15 features ranked by the ReliefF method. Our proposed methodology can be used by clinical staff to make faster and accurate diagnosis of CVDs. Thus, the chances of survival can be significantly increased by early detection and treatment of CVDs.