SOLUBILITY OF CARBON DIOXIDE IN AQUEOUS AND ANHYDROUS MIXTURES OF METHYLDIETHANOLAMINE AND TRIETHYLENE GLYCOL MONOMETHYL ETHER

Mixed solvents are a combination of chemical and physical solvents and are used for the removal of acid gases from gas streams. The solubility of CO₂ in a mixed nonaqueous solution of methyldiethanolamine (MDEA) (50 wt.%) and triethylene glycol monomethj'l ether (TEGMME) (50 wt.%) has been measured at 40°C. The data are compared with solubility data of CO₂ in pure TEGMME. The solubility of CO₂ has been measured at 40°C and 100°C in an aqueous mixed solvent consisting of MDEA (40 wt.%) and TEGMME (40 wt. %) and water (20 wt.%) at partial pressures of CO₂ up to 12 MPa. The solubility results were compared with the solubility in the nonaqueous mixed solvent and previously reported data of aqueous MDEA. The solubility model of Deshmukh and Mather was used to correlate the data.     

Control and Navigation Framework for Quadrotor Helicopters

This paper presents the development of a nonlinear quadrotor simulation framework together with a nonlinear controller. The quadrotor stabilization and navigation problems are tackled using a nested loops control architecture. A nonlinear Backstepping controller is implemented for the inner stabilization loop. It asymptotically tracks reference attitude, altitude and heading trajectories. The outer loop controller generates the reference trajectories for the inner loop controller to reach the desired waypoint. To ensure boundedness of the reference trajectories, a PD controller with a saturation function is used for the outer loop. Due to the complexity involved in controller development and testing, a simulation framework has been developed. It is based on the Gazebo 3D robotics simulator and the Open Dynamics Engine (ODE) library. The framework can effectively facilitate the development and validation of controllers. It has been released and is available at Gazebo quadrotor simulator (2012).     

Lie group method for solving viscous barotropic vorticity equation in ocean climate models

In studying the problem of the nonlinear viscous barotropic non-divergent vorticity equation on $f$- and $\beta$- planes, the method of Lie group has been applied. The method reduces the number of independent variables by one, and consequently, for the case of three independent variables we applied the method successively twice and the nonlinear partial differential equation reduces to ordinary differential equation. Investigation of exact solutions of the viscous barotropic non-divergent vorticity equation on $f$- and $\beta$- planes, via the application of Lie group, provides large classes of new exact solutions which include both Rossby and Rossby–Haurwitz waves as special cases. Also, The Lie symmetries of the viscous barotropic non-divergent vorticity equation with two parameters $F$ and $\beta$, are determined. The possible reductions of the viscous barotropic vorticity equation with two parameters $F$ and $\beta$ have been investigated by means of one- dimensional Lie subalgebras.     

Support vector regression-based model for prediction of behavior stone column parameters in soft clay under highway embankment

In order to have a proper design and analysis for the column of stone in the soft clay soil, it is essential to develop an accurate prediction model for the settlement behavior of the stone column. In the current research, to predict the behavior in the settlement of stone column a support vector machine (SVM) method is developed and examined. In addition, the proposed model has been compared with the existing reference settlement prediction model that using the monitored field data. As SVM mathematical procedure has resilient and robust generalization aptitude and ensures searching for global minima for particular training data as well. Therefore, the potential that support vector regression might perform efficiently to predict the ground soft clay settlement is relatively valuable. As a result, in this study, comparison of two different developed types of SVM method is carried out. Generally, significant reduction in the relative error (RE%) and root mean square error has been achieved. Utilizing nu-SVM-type model through tenfold cross-validation procedure could achieve outstanding performance accuracy level with RE% less than 2% and CR = 0.9987. The study demonstrates high potential for applying SVM in detecting the settlement behavior of SC prediction and ascertains that SVM could be effectively used for settlement stone columns analysis.

     

A new expert system for learning management systems evaluation based on neutrosophic sets

There has been a sudden increase in the usage of Learning Management Systems applications to support learner's learning process in higher education. Many studies in learning management system evaluation are implemented under complete information, while the real environment has uncertainty aspects. As these systems were described by development organizations with uncertainty terms such as vague, imprecise, ambiguity and inconsistent, that is why traditional evaluation methods may not be effective. This paper suggests neutrosophic logic as a better option to simulate human thinking than fuzzy logic because unlike fuzzy logic, it is able to handle indeterminacy of information which expresses the percentage of unknown parameters. As previous studies suggested neutrosophic decision maker and neutrosophic expert systems as future work in ecommerce and e‐learning applications, this paper presents neutrosphic expert system for learning management systems evaluation. Information for building and validating the neutrosophic expert system is collected from five experts using surveys, and then analysis is done by using Fuzzytech 5.54d software. Finally, the comparison between fuzzy expert system and neutrosophic expert system results show that the neutrosophic logic is capable of representing uncertainty in human thinking for evaluating Learning Management Systems.     

Fine-Grained Multivariate Time Series Anomaly Detection in IoT

Sensors produce a large amount of multivariate time series data to record the states of Internet of Things (IoT) systems. Multivariate time series timestamp anomaly detection (TSAD) can identify timestamps of attacks and malfunctions. However, it is necessary to determine which sensor or indicator is abnormal to facilitate a more detailed diagnosis, a process referred to as fine-grained anomaly detection (FGAD). Although further FGAD can be extended based on TSAD methods, existing works do not provide a quantitative evaluation, and the performance is unknown. Therefore, to tackle the FGAD problem, this paper first verifies that the TSAD methods achieve low performance when applied to the FGAD task directly because of the excessive fusion of features and the ignoring of the relationship’s dynamic changes between indicators. Accordingly, this paper proposes a multivariate time series fine-grained anomaly detection (MFGAD) framework. To avoid excessive fusion of features, MFGAD constructs two sub-models to independently identify the abnormal timestamp and abnormal indicator instead of a single model and then combines the two kinds of abnormal results to detect the fine-grained anomaly. Based on this framework, an algorithm based on Graph Attention Neural Network (GAT) and Attention Convolutional Long-Short Term Memory (A-ConvLSTM) is proposed, in which GAT learns temporal features of multiple indicators to detect abnormal timestamps and A-ConvLSTM captures the dynamic relationship between indicators to identify abnormal indicators. Extensive simulations on a real-world dataset demonstrate that the proposed algorithm can achieve a higher F1 score and hit rate than the extension of existing TSAD methods with the benefit of two independent sub-models for timestamp and indicator detection.     

Intelligent System Application to Monitor the Smart City Building Lighting

A smart city incorporates infrastructure methods that are environmentally responsible, such as smart communications, smart grids, smart energy, and smart buildings. The city administration has prioritized the use of cutting-edge technology and informatics as the primary strategy for enhancing service quality, with energy resources taking precedence. To achieve optimal energy management in the multidimensional system of a city tribe, it is necessary not only to identify and study the vast majority of energy elements, but also to define their implicit interdependencies. This is because optimal energy management is required to reach this objective. The lighting index is an essential consideration when evaluating the comfort indicators. In order to realize the concept of a smart city, the primary objective of this research is to create a system for managing and monitoring the lighting index. It is possible to identify two distinct phases within the intelligent system. Once data collection concludes, the monitoring system will be activated. In the second step, the operation of the control system is analyzed and its effect on the performance of the numerical model is determined. This evaluation is based on the proposed methodology. The optimized results were deemed satisfactory because they maintained the brightness index value (79%) while consuming less energy. The intelligent implementation system generated satisfactory outcomes, which were observed 1.75 times on average.     

Neural networks of data inhibiting long memory pattern

We experiment with three neural network models for forecasting to better understand the performance of neural networks for the case when the data exhibits a long memory pattern. To obtain the optimum networks, the effect of network characteristics such as the training parameters, the number of hidden layers, and the testing and training percentages are simulated. The third model, which consists of a combination of individual time series forecasts, provides superior results.     

The solubility of CO2 in triethylene glycol monomethyl ether

The solubility of CO₂ in triethylene glycol monomethyl ether (TEGMME) has been measured at 40, 70 and 100°C at pressures up to 8.8 MPa. The results were correlated with the Peng-Robinson (1976) equation of state. The interaction parameters and Henry's constants were derived.     

Synthesis and characterization of chitosan-coated magnetite nanoparticles using a modified wet method for drug delivery applications

Abstract

Immobilizing various chemical functionalities on the surfaces of magnetite nanoparticles extends these applications without affecting the overall magnetic properties of the coated nanoparticles. In this study, magnetite nanoparticles have been prepared in the presence of chitosan using a simple modified wet method. Composition, morphology and magnetic properties of the chitosan-coated nanoparticles were investigated. Results showed the formation of coating with various thicknesses on the surfaces of the superparamagnetic nanoparticles. This in turn reduced the magnetization of the nanoparticles to some extent. The least particle size obtained in the presence of chitosan was 10?nm.