A Longitudinal Nonparametric Classification Method for Assessing Change in Latent Attributes Over Time

<p>This study proposed a longitudinal Hamming distance discrimination (Long-HDD) method to improve the application of longitudinal cognitive diagnosis in practical teaching by introducing a simple computation and less time-consuming nonparametric classification method—HDD—into longitudinal diagnostic data processing. Compared with the HDD, the proposed method represents correlation or dependence between adjacent time points of the same student using Hamming distance in anticipation of using information from the previous time point to improve the classification accuracy at the current time point. A simulation study was conducted to explore the performance of the proposed method in longitudinal diagnostic data analysis and to compare the performance of the proposed method with the HDD and a parametric longitudinal diagnostic classification model. The findings suggest that (1) the Long-HDD can provide high classification accuracy in longitudinal diagnostic data analysis; (2) compared with the parametric model, the Long-HDD is almost unaffected by sample size and performs better than the parametric model in small sample sizes; and (3) the Long-HDD consumes much less computing time than the parametric model. Overall, the Long-HDD is well suited to analyzing longitudinal diagnostic data and can provide speedy diagnostic feedback due to its convenient computation, which is especially significant in small-scale assessments at the classroom and school levels.</p>

A Longitudinal Nonparametric Classification Method for Assessing Change in Latent Attributes Over Time

<p>This study proposed a longitudinal Hamming distance discrimination (Long-HDD) method to improve the application of longitudinal cognitive diagnosis in practical teaching by introducing a simple computation and less time-consuming nonparametric classification method—HDD—into longitudinal diagnostic data processing. Compared with the HDD, the proposed method represents correlation or dependence between adjacent time points of the same student using Hamming distance in anticipation of using information from the previous time point to improve the classification accuracy at the current time point. A simulation study was conducted to explore the performance of the proposed method in longitudinal diagnostic data analysis and to compare the performance of the proposed method with the HDD and a parametric longitudinal diagnostic classification model. The findings suggest that (1) the Long-HDD can provide high classification accuracy in longitudinal diagnostic data analysis; (2) compared with the parametric model, the Long-HDD is almost unaffected by sample size and performs better than the parametric model in small sample sizes; and (3) the Long-HDD consumes much less computing time than the parametric model. Overall, the Long-HDD is well suited to analyzing longitudinal diagnostic data and can provide speedy diagnostic feedback due to its convenient computation, which is especially significant in small-scale assessments at the classroom and school levels.</p>

Non-parametric synergy modeling of chemical compounds with Gaussian processes

Background Understanding the synergetic and antagonistic effects of combinations of drugs and toxins is vital for many applications, including treatment of multifactorial diseases and ecotoxicological monitoring. Synergy is usually assessed by comparing the response of drug combinations to a predicted non-interactive response from reference (null) models. Possible choices of null models are Loewe additivity, Bliss independence and the recently rediscovered Hand model. A different approach is taken by the MuSyC model, which directly fits a generalization of the Hill model to the data. All of these models, however, fit the dose–response relationship with a parametric model. Results We propose the Hand-GP model, a non-parametric model based on the combination of the Hand model with Gaussian processes. We introduce a new logarithmic squared exponential kernel for the Gaussian process which captures the logarithmic dependence of response on dose. From the monotherapeutic response and the Hand principle, we construct a null reference response and synergy is assessed from the difference between this null reference and the Gaussian process fitted response. Statistical significance of the difference is assessed from the confidence intervals of the Gaussian process fits. We evaluate performance of our model on a simulated data set from Greco, two simulated data sets of our own design and two benchmark data sets from Chou and Talalay. We compare the Hand-GP model to standard synergy models and show that our model performs better on these data sets. We also compare our model to the MuSyC model as an example of a recent method on these five data sets and on two-drug combination screens: Mott et al. anti-malarial screen and O’Neil et al. anti-cancer screen. We identify cases in which the HandGP model is preferred and cases in which the MuSyC model is preferred. Conclusion The Hand-GP model is a flexible model to capture synergy. Its non-parametric and probabilistic nature allows it to model a wide variety of response patterns.

The revised FLORIDyn model: Implementation of heterogeneous flow and the Gaussian wake

In this paper a new version of the FLOw Redirection and Induction Dynamics (FLORIDyn) model is presented. The new model uses the three-dimensional parametric Gaussian FLORIS model and can provide dynamic wind farm simulations at low computational cost under heterogeneous and changing wind conditions. Both FLORIS and FLORIDyn are parametric models which can be used to simulate wind farms, evaluate controller performance and can serve as a control-oriented model. One central element in which they differ is in their representation of flow dynamics: FLORIS neglects these and provides a computationally very cheap approximation of the mean wind farm flow. FLORIDyn defines a framework which utilizes this low computational cost of FLORIS to simulate basic wake dynamics: this is achieved by creating so called Observation Points (OPs) at each time step at the rotor plane which inherit the turbine state. In this work, we develop the initial FLORIDyn framework further considering multiple aspects. The underlying FLORIS wake model is replaced by a Gaussian wake model. The distribution and characteristics of the OPs are adapted to account for the new parametric model, but also to take complex flow conditions into account. To achieve this, a mathematical approach is developed to combine the parametric model and the changing, heterogeneous world conditions and link them with each OP. We also present a computational lightweight wind field model to allow for a simulation environment in which heterogeneous flow conditions are possible. FLORIDyn is compared to SOWFA simulations in three- and nine-turbine cases under static and changing environmental conditions.The results show a good agreement with the timing of the impact of upstream state changes on downstream turbines. They also show a good agreement in terms of how wakes are displaced by wind direction changes and when the resulting velocity deficit is experienced by downstream turbines. A good fit of the mean generated power is ensured by the underlying FLORIS model. In the three turbine case, FLORIDyn simulates 4 s simulation time in 24.49 ms computational time. The resulting new FLORIDyn model proves to be a computationally attractive and capable tool for model based dynamic wind farm control.

Optimization of Credit Activity of a Commercial Bank Based on a Parametric Model

In the past few decades, a line of research focusing on the financial portfolios of banking structures has been actively developed in the world’s economic science. The interest in deposit-and-loan portfolios is caused by the rapid growth of both the banking sector and the entire capital market in the world. This paper presents empirical research in the field of analysis of the credit and investment activities of a commercial bank with an extended set of criteria. The team of authors considered a certain approach to parametric modelling of the optimal banking portfolio taking into account unregulated exogenous (macroeconomic) and endogenous (set by the bank) parameters that affect its structure and composition. As part of the proposed method, a list of monitored parameters of the banking portfolio, which was developed due to financial stability and reliability indicators, was compiled. Accordingly, based on calculations with a modified parametric model and assessment of the level of their financial stability and reliability, shortcomings in the structure and composition of the portfolios of the banking organizations under research were identified with respect to the rationality of resource allocation and the adequacy of equity capital. Thus, it was concluded that taking into account the criteria for managing the banking portfolio, measures of profitability and risk, as well as the reliability of the financial and economic base and financial stability of the bank contributes to the growth of its rating and client base, which is especially important for universal commercial banks. Received: 4 September 2021 / Accepted: 22 November 2021 / Published: 3 January 2022

Parametric model for flora detection in Middle Himalayas

Plant detection forms an integral part of the life of the forest guards, researchers, and students in the field of Botany and for common people also who are curious about knowing a plant. But detecting plants suffer a major drawback that the true identifier is only the flower and in certain species flowering occurs at major time period gaps spanning from few months to over 100 years (in certain types of bamboos). Machine Learning-based systems could be used in developing models where the experience of researchers in the field of plant sciences can be incorporated into the model. In this paper, we present a machine learning-based approach based upon other quantifiable parameters for the detection of the plant presented. The system takes plant parameters as the inputs and will detect the plant family as the output.