Parameters Of Statistical Model Research Articles

A nonstationary runoff frequency analysis for future climate change and its uncertainties

AbstractWith the intensification of climate change, its impact on runoff variations cannot be ignored. The main purpose of this study is to analyse the nonstationarity of runoff frequency adjusted for future climate change in the Luanhe River basin, China, and quantify the different sources of uncertainties in nonstationary runoff frequency analysis. The advantage of our method is the combination of generalized additive models in location, scale, and shape (GAMLSS) and downscaling models. The nonstationary GAMLSS models were established for the nonstationary frequency analysis of runoff (1961–2010) by using the observed precipitation as a covariate, which is closely related to runoff and contributes significantly to its nonstationarity. To consider the nonstationary effects of future climate change on future runoff variations, the downscaled precipitation series in the future (2011–2080) from the general circulation models (GCMs) were substituted into the selected nonstationary model to calculate the statistical parameters and runoff frequency in the future. A variance decomposition method was applied to quantify the impacts of different sources of uncertainty on the nonstationary runoff frequency analysis. The results show that the impacts of uncertainty in the GCMs, scenarios, and statistical parameters of the GAMLSS model increase with increasing runoff magnitude. In addition, GCMs and GAMLSS model parameters have the main impacts on runoff uncertainty, accounting for 14% and 83% of the total uncertainty sources, respectively. Conversely, the interactions and scenarios make limited contributions, accounting for 2% and 1%, respectively. Further analysis shows that the sources of uncertainty in the statistical parameters of the nonstationary model mainly result from the fluctuations in the precipitation sequence. This result indicates the necessity of considering the precipitation sequence as a covariate for runoff frequency analysis in the future.

Uncertainties in the Effects of Climate Change on Maize Yield Simulation in Jilin Province: A Case Study

Measuring the impacts of uncertainties identified from different global climate models (GCMs), representative concentration pathways (RCPs), and parameters of statistical crop models on the projected effects of climate change on crop yields can help to improve the availability of simulation results. The quantification and separation of different sources of uncertainty also help to improve understanding of impacts of uncertainties and provide a theoretical basis for their reduction. In this study, uncertainties of maize yield predictions are evaluated by using 30 sets of parameters from statistical crop models together with eight GCMs with reference to three emission scenarios for Jilin Province of northeastern China. Regression models using replicates based on bootstrap resampling reveal that yields are maximized when the optimum average growing season temperature is 20.1°C for 1990–2009. The results of multi-model ensemble simulations show a maize yield reduction of 11%, with 75% probability for 2040–69 relative to the baseline period of 1976–2005. We decompose the variance so as to understand the relative importance of different sources of uncertainty, such as GCMs, RCPs, and statistical model parameters. The greatest proportion of uncertainty (> 50%) is derived from GCMs, followed by RCPs with a proportion of 28% and statistical crop model parameters with a proportion of 20% of total ensemble uncertainty.

Drivers of Household Decision-Making on Land-Use Transformation: An Example of Woodlot Establishment in Masindi District, Uganda

Land use transformation at the farm level is attributed to household decision-making, reflected by the behavior and activities of smallholder farmers. Unfortunately, household decision-making in local communities and its determinants are site-specific and hardly understood. This study uses multistage purposive selection of households as a unit for the analysis to investigate the transformation from pure agriculture to farm forest mosaics, especially through woodlot establishment. We use key informants, household surveys, and observations to obtain data on decision-making amongst 84 farm households in Nyantonzi parish, Masindi district, Uganda, as an example. Specifically, the study addresses four research questions. Firstly, what is the current status of gender-based decision-making at the household level? Secondly, is decision making within farm households individualistic or collective? Thirdly, which factors are considered to select annual, perennial and tree-crop farm management regimes? Fourthly, what determinants influence the decision-making process and the likelihood of woodlot establishment? A multilevel analysis comprising parametric statistical models and binary logistic regression is applied to assess difference in household natural, physical, human, and social capital, highlight gender roles, and obtain factors associated to selection of crops and determinants of woodlot establishment at the farm level, respectively. Results reveal that gender based decision-making is clearly disaggregated on the basis of husbands and wives and it is individualistic, mostly dominated by husbands with lower participation by wives and other family members. Households consider various factors before making any decisions, e.g., market prices for both annual and perennial crops, food consumption for annual crops, ease of management, and yields from the previous season for the tree-crop management regimes. The likelihood for woodlot establishment is positively influenced by the willingness and intention of households to establish woodlots and relative age of household head. However, knowledge of land use Policies, Laws, and Regulations (PLRs) in relation to tree planting and harvesting and access to non-farm income reduce the likelihood of woodlot establishment. Here, we recommend that current and future forest land restoration initiatives focus on reducing the gender gap and increase women’s participation in decision making, provide market information platforms on wood products, and include woodlot farmers in social organization. Current PLRs on tree tenure should be improved and clearly communicated by using the existing communication assets and social gatherings as channels of change and influence for decision-making.

Adsorption of amoxicillin and tetracycline on activated carbon prepared from durian shell in single and binary systems: Experimental study and modeling analysis

The adsorption mechanism of two antibiotics, namely amoxicillin (AMX) and tetracycline (TCN), on durian shell activated carbon (AC) was investigated in single and binary systems. Adsorption isotherms were determined under the same experimental conditions and they showed that the adsorption capacities of AMX were higher than those of TCN at all tested temperatures suggesting that this adsorbent preferred to remove AMX from the aqueous solution. It was also observed that all adsorption capacities decreased from single to binary systems, which was reasonably explained by an antagonistic adsorption between AMX and TCN on the active sites of AC creating an inhibition effect between these adsorbates. Monolayer and competitive monolayer models were developed via statistical physics theory and they were applied to explain and understand the adsorption mechanism of AMX and TCN via steric and energetic parameters. Results suggested that the parallel and non-parallel orientations of both antibiotics on the adsorbent surface of AC could be possible at tested experimental conditions. Modeling results demonstrated that the numbers of accepted antibiotic molecules per active site of AC varied with linear and inverse trends in single and binary systems, respectively, at all tested temperatures thus corroborating the adsorption inhibition effect caused by both adsorbates. An estimation of adsorption energies was also performed to describe and characterize the interactions between both antibiotics and AC surface. The assessment of the parameters of statistical physics models contributed to the explanation of the single and binary adsorption mechanisms of these water pollutants.

PENGARUH PELATIHAN GOAL SETTING UNTUK MENINGKATKAN MOTIVASI BELAJAR PADA MAHASISWA

ABSTRACT The purpose of this research was to obtain the effect of goal setting training in improving students motivation to learn. The hypothesis of this study that there were a difference between the level of student learning motivation experiment group with control group were given before and after goal setting training. Students are given goal setting training (experiment group) will increase the learning motivation after treated by goal setting training than students who did not receive goal setting training (control group). Subjects in this study amounted to eighteen for the experimental group and also for the control group. Measuring instruments used to measure the level of learning motivation was learning motivation scale. Experimental design used in this study was Pre-Test Post-Test Control Group Design and the process of determining the subject done randomly. The analysis used was a parametric statistical test models of the Independent Sample T - Test to see differences in learning motivation scores between the experimental group with the control group. The results of the pretest data analysis using Independent Sample T-Test known sig = 0.798 ( p > 0.05), and posttest data analysis known sig = 0.000 ( p < 0.005 ). Meanwhile, to see the difference in pretest scores, posttest, and follow-up used paired sample T-test in the experimental group and the control group. The results of data analysis used Paired Sample T-Test on the pretest-posttest scores showed t = -23.884 with a significance level = 0.000 (p< 0.005), the results of the analysis on the posttest-follow-up scores showed t = 4,481 with a significance level = 0.000 (p< 0.005), and the results of the analysis on the pretest score-followup showed t = -20 821 with a significance level = 0.000 (p<0.005). The results of this analysis showed that the experimental group treated with increased motivation to learn after being given the training goal setting than before given the goal setting training. Keywords: goal setting training, learning motivation

Virtual Indicative Broadband over Power Lines Topologies for Respective Subclasses by Adjusting Channel Attenuation Statistical Distribution Parameters of Statistical Hybrid Models (Class Maps) – Part 2: Numerical Results for the Overhead and Underground Medium-Voltage Power Grids

With reference to the initial statistical hybrid model (iSHM) and modified statistical hybrid model (mSHM), the theory of the definition procedure of new virtual indicative distribution BPL topologies by appropriately adjusting the channel attenuation statistical distributions (CASDs) parameters of iSHM and mSHM has been presented in [1]. In this paper, the results of the definition procedure for the OV and UN MV BPL networks are first presented through the prism of the proposed class maps of iSHM and mSHM. Citation: Lazaropoulos, A. G. (2019). Virtual Indicative Broadband over Power Lines Topologies for Respective Subclasses by Adjusting Channel Attenuation Statistical Distribution Parameters of Statistical Hybrid Models (Class Maps) – Part 2: Numerical Results for the Overhead and Underground Medium-Voltage Power Grids. Trends in Renewable Energy, 5, 258-281. DOI: 10.17737/tre.2019.5.3.00100

Virtual Indicative Broadband over Power Lines Topologies for Respective Subclasses by Adjusting Channel Attenuation Statistical Distribution Parameters of Statistical Hybrid Models (Class Maps) – Part 3: The Case of Overhead Transmission Power Grids

In [1], [2], the theoretical framework and the numerical results concerning the class mapping of overhead and underground medium voltage broadband over power lines (OV and UN MV BPL) topologies have been presented on the basis of the recently proposed initial statistical hybrid model (iSHM), modified statistical hybrid model (mSHM) and class map definition procedure. In this paper, all the recent findings regarding the statistical channel modeling and class mapping are first applied to transmission BPL networks; say, OV high voltage (HV) BPL topologies. The numerical results of OV HV BPL networks are compared against the respective ones of OV and UN distribution networks revealing significant similarities and differences. Finally, the impact of considering minimum or maximum capacity value instead of the average one during the definition procedure is investigated as well as the behavior of the total simulation time of class mapping. Citation: Lazaropoulos, A. G. (2019). Virtual Indicative Broadband over Power Lines Topologies for Respective Subclasses by Adjusting Channel Attenuation Statistical Distribution Parameters of Statistical Hybrid Models (Class Maps) – Part 3: The Case of Overhead Transmission Power Grids. Trends in Renewable Energy, 5, 282-306. DOI: 10.17737/tre.2019.5.3.00101

Virtual Indicative Broadband over Power Lines Topologies for Respective Subclasses by Adjusting Channel Attenuation Statistical Distribution Parameters of Statistical Hybrid Models (Class Maps) – Part 1: Theory

Based on a set of indicative overhead and underground medium voltage broadband over power lines (OV and UN MV BPL) topologies, initial statistical hybrid model (iSHM) and modified statistical hybrid model (mSHM) are statistical channel models suitable for the distribution BPL networks. Both iSHM and mSHM statistically process channel attenuation and capacity values of assumed indicative OV and UN MV BPL topologies by exploiting channel attenuation statistical distributions (CASDs). iSHM exploits a set of well-known CASDs (i.e., Gaussian, Lognormal, Wald, Weibull and Gumbel CASDs) while mSHM exploits the Empirical CASD. Each indicative OV and UN MV BPL topology acts as the representative one of a respective OV and UN MV BPL topology class (i.e., rural, suburban, urban and aggravated urban class) that consists of a number of respective statistically equivalent OV and UN MV BPL topologies. The contribution of this paper is the theoretical framework presentation of the creation of new virtual indicative OV and UN MV BPL topologies by appropriately adjusting the parameters of iSHM and mSHM CASDs. These new virtual indicative OV and UN MV BPL topologies will enrich the respective today’s OV and UN MV BPL topology classes with respective OV and UN MV BPL topology subclasses while each subclass will be enriched by a number of respective statistically equivalent OV and UN MV BPL topologies. The procedure of defining new virtual distribution BPL topologies by applying iSHM and mSHM will allow a better capacity study of OV and UN MV BPL topology classes. Apart from the definition procedure of the virtual indicative OV MV and UN MV BPL topologies and their respective virtual subclasses by adjusting CASD parameters of iSHM and mSHM, the contribution of this paper is the class map that analytically describes the taxonomy of distribution BPL topology classes and subclasses. Citation: Lazaropoulos, A. G. (2019). Virtual Indicative Broadband over Power Lines Topologies for Respective Subclasses by Adjusting Channel Attenuation Statistical Distribution Parameters of Statistical Hybrid Models (Class Maps) – Part 1: Theory. Trends in Renewable Energy, 5, 237-257. DOI: 10.17737/tre.2019.5.3.0099

Flexible semiparametric generalized Pareto modeling of the entire range of rainfall amount

AbstractPrecipitation amounts at daily or hourly scales are skewed to the right, and heavy rainfall is poorly modeled by a simple gamma distribution. An important yet challenging topic in hydrometeorology is to find a probability distribution that is able to model well low, moderate, and heavy rainfall. To address this issue, we present a semiparametric distribution suitable for modeling the entire range of rainfall amount. This model is based on a recent parametric statistical model called the class of extended generalized Pareto distributions (EGPDs). The EGPD family is in compliance with extreme value theory for both small and large values, while it keeps a smooth transition between these tails and bypasses the hurdle of selecting thresholds to define extremes. In particular, return levels beyond the largest observation can be inferred. To add flexibility to this EGPD class, we propose to model the transition function in a nonparametric fashion. A fast and efficient nonparametric scheme based on Bernstein polynomial approximations is investigated. We perform simulation studies to assess the performance of our approach. It is compared to two parametric models: a parametric EGPD and the classical generalized Pareto distribution (GPD), the latter being only fitted to excesses above a high threshold. We also apply our semiparametric version of EGPD to a large network of 180 precipitation time series over France.

Synchronization correction-based robust digital image watermarking approach using Bessel K-form PDF

Robustness has played extremely important role in the multiple applications of digital watermarking technology. The reason lies in its influence on the watermarking system’s practicability. As one of the most difficult kinds of digital signal processing for a digital watermark to survive, geometric distortions have become a central problem in digital image watermarking research. Therefore, resigning a greatly robust digital image watermarking approach which can withstand geometric distortions is still a quite challenging work. On account of Bessel K-form (BKF) probability density function (PDF), this paper proposes an optimal synchronization correction-based digital image watermarking method which can resist geometric distortions. This approach consists of watermark embedding, synchronization correction and watermark extraction. Using the quantization index modulation (QIM), the watermark is inserted into the original host images in the nonsubsampled shearlet transform (NSST) domain by adjusting the selected blocks’ low-frequency NSST coefficients. The BKF PDF describes the NSST coefficients’ statistical features. Besides, the BKF statistical model parameters can be used in constructing a compact image characteristic space. Utilizing the compact image feature, the least squares support vector regression (LS-SVR) synchronization correction is performed to estimate the geometric distortions parameters. After LS-SVR synchronization correction, the inverse QIM is performed to recover blindly the inserted watermark. Simulation results demonstrate that the presented digital image watermarking method not only has good imperceptibility performance, but also can well resist challenging common signal processing operations as well as geometric distortions, which can be superior to the most advanced approaches.

Difficulty in inferring microbial community structure based on co-occurrence network approaches

BackgroundCo-occurrence networks—ecological associations between sampled populations of microbial communities inferred from taxonomic composition data obtained from high-throughput sequencing techniques—are widely used in microbial ecology. Several co-occurrence network methods have been proposed. Co-occurrence network methods only infer ecological associations and are often used to discuss species interactions. However, validity of this application of co-occurrence network methods is currently debated. In particular, they simply evaluate using parametric statistical models, even though microbial compositions are determined through population dynamics.ResultsWe comprehensively evaluated the validity of common methods for inferring microbial ecological networks through realistic simulations. We evaluated how correctly nine widely used methods describe interaction patterns in ecological communities. Contrary to previous studies, the performance of the co-occurrence network methods on compositional data was almost equal to or less than that of classical methods (e.g., Pearson’s correlation). The methods described the interaction patterns in dense and/or heterogeneous networks rather inadequately. Co-occurrence network performance also depended upon interaction types; specifically, the interaction patterns in competitive communities were relatively accurately predicted while those in predator–prey (parasitic) communities were relatively inadequately predicted.ConclusionsOur findings indicated that co-occurrence network approaches may be insufficient in interpreting species interactions in microbiome studies. However, the results do not diminish the importance of these approaches. Rather, they highlight the need for further careful evaluation of the validity of these much-used methods and the development of more suitable methods for inferring microbial ecological networks.

Locally optimum image watermark decoder by modeling NSCT domain difference coefficients with vector based Cauchy distribution

Improving the ability of imperceptibility, watermark capacity, and robustness at the same time still remains a challenge within the digital image watermarking community. By modeling the robust nonsubsampled Contourlet transform (NSCT) difference coefficients with vector based Cauchy distribution and employing locally most powerful (LMP) test, we propose a locally optimum image watermark decoder in NSCT domain. We first compute the difference coefficients according to the inter-scale dependency between NSCT coefficients, and investigate the robustness of the NSCT difference coefficients by subjective visual error and objective mean squared error (MSE) terms. We then embed the digital watermark into the significant NSCT difference subband with highest energy by modifying the robust NSCT difference coefficients. At the receiver, by combining the vector based Cauchy probability distribution and LMP test, we propose a locally optimum blind watermark decoder in the NSCT domain. Here, robust NSCT difference coefficients are firstly modeled by employing the vector based Cauchy probability density function (PDF), where the Cauchy marginal statistics and various strong dependencies of NSCT coefficients are incorporated. Then the statistical model parameters of vector based Cauchy PDF are estimated using second-kind statistics approach. And finally a blind image watermark decoder is developed using vector based Cauchy PDF and LMP decision rule. We conduct extensive experiments to evaluate the performance of the proposed blind watermark decoder, in which encouraging results validate the effectiveness of the proposed technique, in comparison with the state-of-the-art approaches recently proposed in the literature.

Principles and Criteria for using Statistical Parametric Models and Conditional Models for Valuation of Multi-Component Real Estate

Abstract The complexity of multi-component real properties results from the possibility of identifying various components in legal, physical or functional terms. The possibility of distinguishing various functional elements of real properties, combined with the specificity resulting from their market properties, is problematic when applying the comparative approach to real estate valuation. In this case, the valuation procedure can be implemented using statistical models: the parametric model or the conditional one. This research paper demonstrates the construction of the parametric and conditional models taking into account the geometric and pricing attributes of multi-component real estate. The authors paid attention to adjusting the models to the available market data. They also specified the conditions for the use of statistical models in the real estate valuation process. Based on the analytical and accounting considerations, the estimation criteria for the parametric model and the conditional model were defined, which allow the correct application of these models at the stages of the real estate market analysis and the real estate valuation process.

From Hessian to Weitzenböck: manifolds with torsion-carrying connections

We investigate affine connections that have zero curvature but not necessarily zero torsion. Slightly generalizing from what is known as Weitzenbock connections, such non-flat connections (we call “pseudo-Weitzenbock connections”) can be constructed from any frame (a set of linearly independent vector fields), not just the orthonormal frames, with their torsions vanishing if and only if the frame is a coordinate frame. In such situations, the notion of biorthogonal frames generalizes the notion of biorthogonal coordinates of a Hessian manifold, with respect to any given Riemannian metric g (not necessarily Hessian). Our main theorem shows that the pair of pseudo-Weitzenbock connections, each adapted to one of the pair of g-biorthogonal frames, are g-conjugate to each other. As a result, the pseudo-Weitzenbock connection pair generalize dually flat connections characteristic of Hessian manifolds, by being both curvature-free yet admitting (generally unequal) torsions. These results allow us to construct a pseudo-Weitzenbock connection for the manifold of parametric statistical models and treat it as a “statistical manifold admitting torsion”.

In Situ Liquid SAXS Studies on the Early Stage of Calcium Carbonate Formation

AbstractCalcium carbonate is a model system to investigate the mechanism of solid formation by precipitation from solutions, and it is often considered in the debated classical and nonclassical nucleation mechanism. Despite the great scientific relevance of calcium carbonate in different scientific areas, little is known about the early stage of its formation. Therefore, contactless devices are designed that are capable of providing informative investigations on the early stages of the precipitation pathway of calcium carbonate in supersaturated solutions using classical scattering methods such as wide‐angle X‐ray scattering (WAXS) and small‐angle X‐ray scattering (SAXS) techniques. In particular, SAXS is exploited for investigating the size of entities formed from supersaturated solutions before the critical conditions for amorphous calcium carbonate (ACC) nucleation are attained. The saturation level is controlled and kept constant by mixing four diluted solutions (i.e., NaOH, CaCl2, NaHCO3, H2O) at constant T and pH. The scattering data are collected on a liquid jet generated about 75 s after the mixing point. The data are modeled using parametric statistical models providing insight about the size distribution of denser matter in the liquid jet. Theoretical implications on the early stage of solid formation pathway are inferred.

Role of consistent parameter sets in an assessment of the α -particle optical potential below the Coulomb barrier

Background: Further studies of high-precision measurements of alpha-induced reaction data below the Coulomb barrier have still raised questions about the alpha-particle optical model potential (OMP) within various mass ranges, i.e. for 64Zn, 108Cd, 113,115In, 121,123Sb, and 191,193Ir target nuclei. Purpose: The accuracy as well as eventual uncertainties and/or systematic errors of using a previous optical potential are much better considered by analysis of such accurate data. Method: Statistical model (SM) calculations of the (alpha,x) reaction cross sections have been carried out using model parameters which were previously obtained by analyzing independent data, particularly gamma-ray strength functions, and taking into account their uncertainties and questions of extrapolation for nuclei without similar data. Results: Consistent description of the recent alpha-induced reaction data is provided by the above-mentioned optical potential with no empirical rescaling factors of either its own parameters or the gamma and/or nucleon widths. Effects of still uncertain SM parameters on calculated alpha-induced reaction cross sections and conclusions on the alpha-OMP can be now discussed due to unprecedented precision of the new data. Conclusions: The alpha-particle optical potential has been confirmed at incident energies below the Coulomb barrier using statistical-model parameters validated through a former analysis of independent data.

Identification of credit risk based on cluster analysis of account behaviours

Assessment of risk levels for existing credit accounts is important to the implementation of bank policies and offering financial products. This article uses cluster analysis of behaviour of credit card accounts to help assess credit risk level. Account behaviour is modelled parametrically and we then implement the behavioural cluster analysis using a recently proposed dissimilarity measure of statistical model parameters. The advantage of this new measure is the explicit exploitation of uncertainty associated with parameters estimated from statistical models. Interesting clusters of real credit card behaviours data are obtained, in addition to superior prediction and forecasting of account default based on the clustering outcomes.

Interpretation of the adsorption mechanism of Reactive Black 5 and Ponceau 4R dyes on chitosan/polyamide nanofibers via advanced statistical physics model

Herein, a chitosan/polyamide nanofibers (CPN)-based adsorbent is employed to interpret the adsorption mechanism of two dyes, namely Reactive Black 5 (RB5) and Ponceau 4R (P4R), at different temperatures. Based on experimental results, the adsorption isotherms of RB5 and P4R dyes indicate that the separation process tends to a saturation plateau at high concentration, also suggesting that the dyes adsorption process occurs with the formation of a finite number of layers. To provide a reasonable explanation of the dyes adsorption mechanisms, three statistical physics models are used and discussed. The advanced modelling analysis reveals that the adsorption of both dyes occurs by the formation of two different adsorbed layers on CPN surface. Based on the application of the best statistical physics adsorption model, it was deduced that both dyes were anchored with horizontal and non-horizontal positions on CPN surface depending on temperature, which was concluded by investigation the captured number of dye molecules per CPN adsorbent site during the adsorption process. Based on these theoretical results, it was noted that the adsorption capacities at saturation followed this sequence: Q (P4R) > Q (RB5) for all investigated temperatures (i.e., 222.4, 261.3, 352.8, 482.2 mg/g for P4R and 198.6, 267.7, 288.2, 352.5 mg/g for RB5), indicating that CPN adsorbent was more effective for the P4R dye removal. The adsorption energies are studied to explain the difference in the interactions between dyes and adsorbent surface (P4R-CPN, RB5-CPN), and between these dyes (P4R-P4R, RB5-RB5), indicating that the removal of these adsorbates was based on a physisorption mechanism where a desorption process is possible. Comparing the estimated parameters of the best statistical physics model, it was concluded that both steric and energetic factors are mainly responsible for the adsorption of P4R and RB5 dyes on CPN.

Simultaneous estimation of parameter uncertainties and disturbance trajectory for robotic manipulator

In this work, a systematic approach is proposed to estimate the disturbance trajectory using a new generalized Lyapunov matrix valued function of the joint angle variables and the robot’s physical parameters using the maximum likelihood estimate (MLE). It is also proved that the estimated disturbance error remains bounded over the infinite time interval. Here, the manipulator is excited with a periodic torque and by the position and velocity data collected at discrete time points construct an ML estimator of the parameters at time \( t + dt \). This process is carried over hand in hand in a recursive manner, thus resulting in a novel unified disturbance rejection and parameter estimation in a general frame work. These parameter estimates are then analyzed for mean and covariance and compared with the Cramer Rao Lower Bound (CRLB) for the parametric statistical model. Using the Lyapunov method, convergence of the “disturbance estimation error” to zero is established. We assume that a Lyapunov matrix dependent on the link angle and form the energy corresponding to this matrix as a quadratic function of the disturbance estimate error. Using the dynamics of the disturbance observer, the rate of change of the Lyapunov energy is evaluated as a quadratic form in the disturbance error. This quadratic form is negative definite for the angular velocity in a certain range and for a certain structured form of the Lyapunov energy matrix. The most general form of the Lyapunov matrix is obtained that guarantees negative rate of increase of the energy and a better bound on the disturbance estimation error convergence rate to zero. This is possible only because we have used the most general form of the Lyapunov energy matrix.

Artificial Intelligence in Nuclear Cardiology: Adding Value to Prognostication

Radionuclide myocardial perfusion imaging (MPI) continues to be an accurate and reproducible method of diagnosing obstructive coronary artery disease (CAD) with predictive, prognostic, and economic value. We review the evolutionary potential of machine learning (ML), a subset of artificial intelligence, as an adjunct to MPI. Applying the broad scope of ML, including the integration of deep learning, can leverage the knowledge representation and automated reasoning to detect and extrapolate patterns from high-dimensional features of MPI. There is growing evidence to suggest superior abilities of ML over parametric statistical models for predicting the presence of obstructive CAD, the need for revascularization, and the occurrence of major adverse cardiac events including cardiac death. ML is uniquely positioned to provide the next great advancement in the field of nuclear cardiology for improving patient-specific risk stratification.