Thermal transport of hybrid nanofluids with entropy generation: A numerical simulation

In this research, thermal radiation, entropy generation and variable thermal conductivity effects on hybrid nanofluids by moving sheet are analyzed. The liquid is placed by stretchable flat wall that is flowing in a nonlinear pattern. Thermal conductivity changes with temperature governed by thermal radiation and MHD is incorporated. Approximations of boundary layer correspond to a set of PDEs which are then changed into ODEs by considering suitable variables. The resulting ODEs are solved using the bvp4c method. The implication with considerable physical characteristics on temperature, entropy generation and velocity profile is graphically represented and numerically discussed. Entropy generation increases for increasing Reynolds number, velocity slip parameter, Brinkman number and magnetic parameter. Scientists have recently established a rising interest in the importance of nanoparticles due to their numerous technical, industrial and commercial uses. The provided insights can be used in extrusion application areas, macromolecules, biomimetic systems, energy production and industrial process improvements.

ML-based group method of data handling: an improvement on the conventional GMDH

Machine learning (ML) has been recognized as a feasible and reliable technique for the modeling of multi-parametric datasets. In real applications, there are different relationships with various complexities between sets of inputs and their corresponding outputs. As a result, various models have been developed with different levels of complexity in the input–output relationships. The group method of data handling (GMDH) employs a family of inductive algorithms for computer-based mathematical modeling grounded on a combination of quadratic and higher neurons in a certain number of variable layers. In this method, a vector of input features is mapped to the expected response by creating a multistage nonlinear pattern. Usually, each neuron of the GMDH is considered a quadratic partial function. In this paper, the basic structure of the GMDH technique is adapted by changing the partial functions to enhance the complexity modeling ability. To accomplish this, popular ML models that have shown reasonable function approximation performance, such as support vector regression and random forest, are used, and the basic polynomial functions in the GMDH are replaced by these ML models. The regression feasibility and validity of the ML-based GMDH models are confirmed by computer simulation.

Forecasting Currency in East Java: Classical Time Series vs. Machine Learning

Most research about the inflow and outflow currency in Indonesia showed that these data contained both linear and nonlinear patterns with calendar variation effect. The goal of this research is to propose a hybrid model by combining ARIMAX and Deep Neural Network (DNN), known as hybrid ARIMAX-DNN, for improving the forecast accuracy in the currency prediction in East Java, Indonesia. ARIMAX is class of classical time series models that could accurately handle linear pattern and calendar variation effect. Whereas, DNN is known as a machine learning method that powerful to tackle a nonlinear pattern. Data about 32 denominations of inflow and outflow currency in East Java are used as case studies. The best model was selected based on the smallest value of RMSE and sMAPE at the testing dataset. The results showed that the hybrid ARIMAX-DNN model improved the forecast accuracy and outperformed the individual models, both ARIMAX and DNN, at 26 denominations of inflow and outflow currency. Hence, it can be concluded that hybrid classical time series and machine learning methods tend to yield more accurate forecasts than individual models, both classical time series and machine learning methods.

The Optical Performance of Holmium Oxide Doped Tellurite Glass for Potential Optical Fiber

Borotellurite glass had been widely applied in the field of optical communications and devices. In this work, holmium oxides doped borotellurite glass had been successfully fabricated via conventional melt-quenched technique. The structural properties of holmium doped tellurite glass were found using x-ray diffraction (XRD) method. The nonexistence of sharp peaks in XRD pattern shows that the inclusion of holmium tellurite glass leads to the formation long range of disorderness. The optical properties of the glass system such as refractive index and optical band gap energy are investigated using UV-Vis spectrophotometer. The value of refractive index is found in nonlinear trend along with holmium oxides concentration. It is found that the refractive index is more than 2 at 0.01, 0.03 and 0.04 of holmium concentrations. The optical band gap energy was found in similar trend with refractive index which is in nonlinear pattern.

GLOBAL VALUE CHAINS (GVCS) AND ECONOMIC GROWTH: A NONLINEAR ANALYSIS

This study examines the relationship between economic growth and participation in global value chains (GVCs) and demonstrates that the U-shaped nonlinear pattern of GVCs could be more effective than the simple linear pattern of GVCs in terms of economic growth in high- and middle-income economies. The U-shaped nonlinear pattern expresses that there are decreasing foreign-dominated GVCs (increasing high value-added domestic value chains) for building local value chain and then raise the GVCs participation to benefit at a better position in GVCs. This paper investigates a panel of 63 advanced and emerging economies and obtained significant evidence by using systemic quantitative analysis. This research suggests that emerging markets should decrease foreign-dominated GVCs (increase high value-added domestic value chain) and then raise the participation of the GVC for economic growth.