Assessing the drivers and solutions of green innovation influencing the adoption of renewable energy technologies.

The degradation of the environment in China is accelerating along with economic expansion. Adoption of renewable energy technologies (RETs) is crucial for reducing the adverse impacts of economic growth on the environment and fostering sustainable development. This study attempts to identify the green innovation drivers and sub-drivers that affect the adoption of RETs in China and provide solutions for boosting their implementation. The study prioritized the drivers, sub-drivers, and strategies of green innovation by combining the Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) methods. In the study, the triple bottom line (TBL) approach has been used to determine the economic, societal, and environmental driving forces. The study also suggests strategies for encouraging the use of RETs. The results of the AHP method revealed that economics is the most crucial driver, with a weight of 0.376, followed by environmental (0.332), and social (0.291) drivers. The findings of the SAW method indicated that government green innovation initiatives, consumer initiatives, and industry initiatives are the most significant strategies for deploying RETs in China. This study has important theoretical and practical ramifications for encouraging China to adopt RETs. The suggested approaches can help researchers, business professionals, and policymakers promote sustainable development in China.

Prediction of Tool Eccentricity Effects on the Mechanical Properties of Friction Stir Welded AA5754-H24 Aluminum Alloy Using ANN Model.

The current study uses three different pin eccentricities (e) and six different welding speeds to investigate the impact of pin eccentricity on friction stir welding (FSW) of AA5754-H24. To simulate and forecast the impact of (e) and welding speed on the mechanical properties of friction stir welded joints for (FSWed) AA5754-H24, an artificial neural network (ANN) model was developed. The input parameters for the model in this work are welding speed (WS) and tool pin eccentricity (e). The outputs of the developed ANN model include the mechanical properties of FSW AA5754-H24 (ultimate tensile strength, elongation, hardness of the thermomechanically affected zone (TMAZ), and hardness of the weld nugget zone (NG)). The ANN model yielded a satisfactory performance. The model has been used to predict the mechanical properties of the FSW AA5754 aluminum alloy as a function of TPE and WS with excellent reliability. Experimentally, the tensile strength is increased by increasing both the (e) and the speed, which was already captured from the ANN predictions. The R2 values are higher than 0.97 for all the predictions, reflecting the output quality.

A Machine-Learning-Based Approach for Predicting Mechanical Performance of Semi-Porous Hip Stems.

Novel designs of porous and semi-porous hip stems attempt to alleviate complications such as aseptic loosening, stress shielding, and eventual implant failure. Various designs of hip stems are modeled to simulate biomechanical performance using finite element analysis; however, these models are computationally expensive. Therefore, the machine learning approach is incorporated with simulated data to predict the new biomechanical performance of new designs of hip stems. Six types of algorithms based on machine learning were employed to validate the simulated results of finite element analysis. Afterwards, new designs of semi-porous stems with outer dense layers of 2.5 and 3 mm and porosities of 10-80% were used to predict the stiffness of the stems, stresses in outer dense layers, stresses in porous sections, and factor of safety under physiological loads using machine learning algorithms. It was determined that decision tree regression is the top-performing machine learning algorithm as per the used simulation data in terms of the validation mean absolute percentage error which equals 19.62%. It was also found that ridge regression produces the most consistent test set trend as compared with the original simulated finite element analysis results despite relying on a relatively small data set. These predicted results employing trained algorithms provided the understanding that changing the design parameters of semi-porous stems affects the biomechanical performance without carrying out finite element analysis.