Quantifying ecological thresholds from response surfaces

Titania nanotube arrays are of great interest due to their nanotubular structure and size-dependent properties. Self-organized TiO2 nanotube arrays could be simply synthesized using anodization method. Many researchers had synthesized TiO2 nanotube arrays with various pore-sizes and lengths through changing the electrolyte type and concentration, pH, applied voltage, and electrolyte composition. Despite the relatively large bandgap of TiO2 (3.2 eV), rapid recombination of electron hole generated during photocatalytic reaction has limited the efficiency of the photocatalytic reactions under the visible light. Many researchers have been conducted to improve the photocatalytic efficiency of the TiO2 nanotube arrays by doping with metal and nonmetal ions. In this work, TiO2 nanotubes were synthesized using the anodization in the variety conditions such as electrolyte concentration, anodization voltage, and time. The photoreactivity of the TiO2 nanotube before and after doping was investigated. The morphology, microstructure, photo micro kinetics and photo absorption of the doped–TiO2 nanotube were characterized using SEM/EDX, XRD, and UV–Vis spectrum. The decolorization of C.I. acid red 14 dye (AR 14) by Photocatalytical process based on TNT films was calculated in a batch type photoreactor. Response surface methodology (RSM) was used to recognize single and multi-effects of the main independent parameters (anodizing time, anodizing voltage, N2 treatment time, and catalyst surface area) on the decomposition rate. A central composite design (CCD) was used to maximize the photoreaction of AR 14 efficacy. A second-order empirical relationship between the response and independent variables was extracted. The results of the analysis of variance (ANOVA) demonstrated a high coefficient of determination value (R2=0.980). Optimum decolorization rate was calculated and experimentally conformed. The optimum anodizing time, anodizing voltage, N2 treatment time, and catalyst surface area were found to be 200 s, 20 V, 2077 h and 5 cm2, respectively. Under the optimal estimated conditions, the highest bleaching efficiency of AR 14 (>99%) was obtained at the lowest reaction time. The results clearly proved that, the response surface methodology could be the suitable method to optimize and scale up the photo catalytically conditions in lab and industrial cases. Also, graphical 2-3 D plots were employed to obtain the optimum point and continuous response.

Prescribing and consuming drugs more than necessary is considered as polypharmacy, which is both wasteful and harmful. The purpose of this paper is to establish an innovative data mining framework for analyzing physicians’ prescriptions regarding polypharmacy. The approach consists of three main steps: pre-modeling, modeling, and post-modeling. In the first step, after collecting and cleaning the raw data, several novel physicians’ features are extracted. In the modeling step, two popular decision trees, i.e., C4.5 and Classification and Regression Tree (CART), are applied to generate a set of If-Then rules in a tree-shaped structure to detect and describe physicians’ features associated with polypharmacy. In a novel approach, the response surface method (RSM) as a tool for hyper-parameter tuning is simultaneously applied along with correlation-based feature selection (CFS) to enhance the performance of the algorithms. In the post-modeling step, the discovered knowledge is visualized to make the results more perceptible, then is presented to domain experts to evaluate whether they make sense or not. The framework has been applied to a real-world dataset of prescriptions. The results have been confirmed by the experts, which demonstrates the capabilities of the data mining framework in the detection and analysis of polypharmacy.

When an autonomous robot is to learn its behavior, whether an appropriate state space is available or not is a critical issue for the flexibility and efficiency of the learning process. What is problematic is that it is usually very difficult to prepare such an ideal state space manually beforehand. We propose a new state space method. With this, behavior-based robots can autonomously rebuild their state spaces after they accumulate behavior experience using initial state spaces. This reconstruction approach is more advantageous than the conventional state space construction methods or incremental state partitioning methods in that it achieves both the efficiency in the learning process and the optimality of the resultant behavior performance.

Newton’s law of cooling requires a reference temperature (Tref) to define the heat-transfer coefficient (h). For external flows with multiple temperatures in the freestream, obtaining Tref is a challenge. One widely used method, referred to as the adiabatic-wall (AW) method, obtains Tref by requiring the surface of the solid exposed to convective heat transfer to be adiabatic. Another widely used method, referred to as the linear-extrapolation (LE) method, obtains Tref by measuring/computing the heat flux (qs′′) on the solid surface at two different surface temperatures (Ts) and then linearly extrapolating to qs′′=0. A third recently developed method, referred to as the state-space (SS) method, obtains Tref by probing the temperature space between the highest and lowest in the flow to account for the effects of Ts or qs′′ on Tref. This study examines the foundation and accuracy of these methods via a test problem involving film cooling of a flat plate where qs′′ switches signs on the plate’s surface. Results obtained show that only the SS method could guarantee a unique and physically meaningful Tref where Ts=Tref on a nonadiabatic surface qs′′=0. The AW and LE methods both assume Tref to be independent of Ts, which the SS method shows to be incorrect. Though this study also showed the adiabatic-wall temperature, TAW, to be a good approximation of Tref (<10% relative error), huge errors can occur in h about the solid surface where |Ts−TAW| is near zero because where Ts=TAW, qs′′≠0.

Chemical EOR is one of the promising methods to improve the oil recovery. However, due to high cost of the process, there are challenges to minimize the cost and maximize the oil recovery. Some influencing parameters should be taken into account in a systematic approach to find their impact on oil recovery and accordingly optimizing the process. In this study, we present a robust optimization workflow of alkaline-surfactant (AS) flooding into a thin clastic reservoir of a field in the Malay Basin. There are coreflood experiments and pilot tests on this field that can be quite helpful to provide a basis to find out the appropriate range of input parameters. Optimization work is based on response surface methodology (RSM) and particle swarm optimization (PSO) technique that aid us to indicate the optimum oil recovery from chemical flooding. In order to get the utmost advantage of this workflow, the waterflooding should be optimized prior to the chemical flooding optimization to maximize the sweep efficiency and oil recovery from the chemical flood. Evaluation of coreflood and pilot tests indicated that some parameters need supplementary evaluation to investigate their effect on reservoir performance and flow dynamics. These parameters include residual oil reduction by chemical, relative permeability curves, chemicals adsorption, chemical concentration, slug size, injection rate, and initiation time of chemical injection. Based on the result of tornado chart, residual oil reduction and injection rate exhibited highest and lowest impact on oil recovery. RSM was used to explore the relationship between input variables and objective function. Some design parameters such as chemical concentration, slug size and initiation time were examined in this stage. Afterwards, proxy models have been built using polynomial regression and neural network methods. The results showed that the proxy model by neural network method revealed better performance for prediction of the simulation results. The proxy model was used to calculate the oil recovery for any combination of input parameters. Besides, it was used to assess the parameter sensitivity and identify the impact of any input parameter on oil recovery. At the next stage, PSO method was utilized to optimize the oil recovery by chemical flooding. It was found that the optimized water injection rate and pattern for water flooding scenario need further optimization to improve the sweep efficiency and thereby oil recovery by AS flooding at later stage. Running numerous simulation cases is normally expected to optimize the process by conventional methods and the proposed PSO approach can be used to reduce the number of runs significantly. Sensitivity analysis provided a very good understanding about reserve ranges for the different influential parameters. Optimizing the cost of chemical flooding and improving oil recovery are other outcomes of this study.

A State Space Boundary Element Method with Analytical Formulas for Nearly Singular Integrals

Deep reinforcement learning based planning method in state space for lunar rovers

The ability to manufacture accurate parts in single point incremental forming is dependent on the capability to properly predict accuracy response surfaces of individual features and feature interaction combinations formed using uncompensated tool paths. Recent studies show that the accuracy profiles obtained are dependent on the choice of material used for forming, in terms of magnitude, geometric shape and nature of errors (under forming and over forming). In this paper, an attempt is made to capture the effect of material properties on the accuracy response surfaces. The response surfaces are modeled using Multivariate Adaptive Regression Splines (MARS), which is a non-parametric multivariate regression technique that helps generating continuous response surfaces. The MARS functions are based on process and feature specific geometric parameters. A set of features and feature interactions for which the response surface dependence on material properties is well predicted is used to illustrate the applicability of the MARS method for predicting the accuracy. An in-process stereo camera system is used to measure the displacement fields for different materials using digital image correlation (DIC) and understand the material dislocation mechanism. Improvements in accuracy for different sheet metal materials based on the predicted response surfaces are then discussed.

The modeling phase of response surface methodology (RSM) involves the application of regression techniques to fitting a curve to the data generated from a designed experiment. The model-robust regression 2 (MRR2) method is a semi-parametric regression approach that combines portions of estimates from both the parametric and the nonparametric regression approaches via a mixing parameter. However, the robustness of the estimates from the MRR2 approach depends largely on the choice of bandwidth. Utilizing the cross-validation approach to bandwidth selection, we propose a methodology for deriving a data-driven function that generates local bandwidths for the MRR2 approach. Using two examples from the literature and a simulation study, we show that, in comparison with other regression methods, the results obtained from the MRR2 approach utilizing the proposed function offer remarkable improvements in the goodness-of-fit statistics.

<p>Los métodos de regresión se utilizan ampliamente en el ámbito forestal para la predicción y el cartografiado de las variables de estructura y combustibilidad. En este artículo se evalúan diferentes modelos de regresión (lineal, no lineal, árboles de regresión y ensemble). Como variables independientes se utilizaron métricas extraídas de datos LiDAR full-waveform, mientras que los valores de las variables dependientes se generaron a partir de modelos basados en datos de campo obtenidos para 78 parcelas de 16 m de radio. Se llevaron a cabo transformaciones de las variables dependientes e independientes con selección de atributos para evaluar su influencia en la predicción de la variable respuesta. Con el fin de verificar diferencias significativas y ordenar los modelos de regresión se emplearon los tests no paramétricos de Wilcoxon y Friedman, y el análisis post-hoc o los tests de comparación post-hoc por pares, como el de Nemenyi, para el test de Friedman. Las regresiones basadas en la transformación de la variable dependiente, como raíz cuadrada o logaritmo, o en la transformación de las variables independientes, obtuvieron un incremento de la R<sup>2</sup> de hasta un 6% con respecto a la regresión lineal. Mediante el método CART (Classification and Regression Tree) se obtuvieron resultados discretos, si bien su uso puede estar indicado para la categorización o estratificación. Con el método basado en la transformación de la variable dependiente mediante raíz cuadrada se consiguieron los mejores resultados comparativos en la predicción de variables forestales, excepto para el volumen. Sin embargo, su uso no siempre implica una mejora significativa con respecto a los otros métodos de regresión usados en este trabajo.</p>

The state space model provides an unifying framework for analyzing time series. For the classical Box–Jenkins analysis, state space methods are useful when estimating the autoregressive and moving average coefficients by exact maximum likelihood and when missing values are present. In the case of unobserved components time series models with interpretable components such as trend, seasonal, cycle and irregular, state space methods provide the tools for estimation, signal extraction and diagnostic checking. Indeed, all linear time series models can be cast into state space including dynamic regression models and cubic spline smoothing models.

The pulp and paper industry in Indonesia is the tenth largest producer in the world, with Acacia and Eucalyptus as the main genera used for production; however, limited publications exist related to Eucalyptus growth models in Indonesia compared with other regions. Time-based models have been developed in which height, stand density, and basal area are predicted based on initial conditions and age. In contrast, a state–space approach utilizes the rate of change of these three state variables. Previous direct comparisons of these two approaches are generally limited. Consequently, the objective of this study was to compare two stand-level growth modeling approaches for Eucalyptus hybrid species on Sumatera (Sumatra) Island using both time-based and state–space methods. Our results indicate that dynamic models using either time-based or state–space approaches are adequate for predicting stand parameters to rotation age. A modified Bazukis matrix indicated that the behavior of both methods produced reliable predictions that were biologically reasonable in terms of stand development; however, the time-based approach provided better performance than the state–space approach on a variety of equivalence tests and goodness-of-fit statistics. Overall, the analysis highlights the advantages and disadvantages of these two commonly used, yet highly contrasting, stand-level growth modeling approaches, which need further consideration and evaluation.

A Practitioner's Guide and MATLAB Toolbox for Mixed Frequency State Space Models

Time-series analysis of pressure fluctuations in gas–solid fluidized beds – A review

Various analytical techniques are available for modeling multivariate time series data. One such approach is the State Space Model, which can be employed to model this type of data. In this study, the data to be analyzed are data on the Indonesia Rupiah (IDR) exchange rate (ExR) against the US Dollar (USD), oil and gas exports (OGE), money supply (MS) and non-oil and gas exports (non-OGE) from January 2008 to December 2019. The aim of this study is to identify the most suitable state space model for the given data. In this research, the state space method will be applied to multivariate time series data, with the state space represented in the Vector Autoregressive (VAR) model to explore the interrelationships among groups of observed variables. The VAR model is a statistical technique used to analyze the relationships between variables in the dataset, employing the Granger Causality Test. The state space model is utilized to model and forecast multiple interconnected time series, where the variables exhibit dynamic interactions and to examine additional unobserved variables in the time series data. Based on the analysis results and the minimum value of the Akaike Information Criterion (AIC), the optimal VAR model identified is the VAR (6) model. The results of forecasting values using the state space model show that the predicted values and the real values for the state space model are very closed to each other.