An Integrated Hybrid Methodology for Estimation of Absorptivity and Interface Temperature in Laser Transmission Welding

This study reports a new hybrid integrated technique to predict the absorptivity of absorber and the interface temperature of the joint in laser transmission welding. The new approach is more robust as the numerical model is confirmed through experimental observations initially with weld width and further with surface temperature. Experiments are performed on a polycarbonate sheets with electrolytic iron powder (EIP) as an absorber. The surface temperature and weld width are measured from the experiments. A transient 3-D finite element-based numerical model is developed for heat transfer analysis. The variation of heat flux with stand-off distance is also considered to enhance the accuracy of the computed results. The absorptivity is tuned in the numerical model so that the numerical weld width is in close conjunction with the experimental weld width. The numerical model is validated by comparing the upper surface temperatures at the center, measured in the experiments using infrared thermography. The results indicate that the surface temperatures in the numerical model are in good agreement with experimental observations, and the average error is less than 6%. The interface temperatures are estimated after the validation of the numerical model.

Integrating InSAR Observables and Multiple Geological Factors for Landslide Susceptibility Assessment

Due to extreme weather, researchers are constantly putting their focus on prevention and mitigation for the impact of disasters in order to reduce the loss of life and property. The disaster associated with slope failures is among the most challenging ones due to the multiple driving factors and complicated mechanisms between them. In this study, a modern space remote sensing technology, InSAR, was introduced as a direct observable for the slope dynamics. The InSAR-derived displacement fields and other in situ geological and topographical factors were integrated, and their correlations with the landslide susceptibility were analyzed. Moreover, multiple machine learning approaches were applied with a goal to construct an optimal model between these complicated factors and landslide susceptibility. Two case studies were performed in the mountainous areas of Taiwan Island and the model performance was evaluated by a confusion matrix. The numerical results revealed that among different machine learning approaches, the Random Forest model outperformed others, with an average accuracy higher than 80%. More importantly, the inclusion of the InSAR data resulted in an improved model accuracy in all training approaches, which is the first to be reported in all of the scientific literature. In other words, the proposed approach provides a novel integrated technique that enables a highly reliable analysis of the landslide susceptibility so that subsequent management or reinforcement can be better planned.

An Intelligent Combined Visual Navigation Brain Model/GPS/MEMS–INS/ADSFCF Method to Develop Vehicle Independent Guidance Solutions

This paper presents an integrated navigation system that can function more efficiently than an inertial navigation system (INS), the results of which are not precise enough because of drifts caused by accelerometers. The paper’s proposed approach depends primarily on integrating micro-electrical-mechanical system (MEMS)-INS smartphone integrated sensors, the Global Positioning System (GPS), and the visual navigation brain model (VNBM) to enhance navigation in bad weather conditions. The recommended integrated navigation model, using an adaptive DFS combined filter, has been well studied and tested under severe climate conditions on reference trajectories. This integrated technique can easily detect and disable less accurate reference sources (GPS or VNBM) and activate a more accurate one. According to the results, the proposed integrated data fusion algorithm offers a reliable solution for errors in the previous strategies. Furthermore, compared to the pure MEMS–INS method, the proposed system reduces navigational errors by approximately 93.76 percent, whereas the conventional centralized Kalman filter technique reduces such errors by 82.23 percent.

A comparative multi criteria decision analysis of football teams: evidence on FIFA world cup

Purpose World Cup tournament is one of the most popular international organizations in football. The purpose of this paper is to investigate the overall performance of World Cup 2018 teams via multi-criteria decision-making (MCDM) approaches. Design/methodology/approach The presented approach adopts entropy integrated Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Weighted Aggregated Sum Product Assessment (WASPAS) approaches to weight the criteria and evaluate the performance of World Cup 2018 teams. Initially, the authors compute weight criteria via Shannon Entropy method. Then, the authors compute and compare the results of TOPSIS and WASPAS methods so as to evaluate the performance of World Cup 2018 teams. Findings According to TOPSIS and WASPAS results, Belgium has demonstrated better performance in comparison to other teams. As per to the empirical results, both methods have shown a significant harmony in terms of performance figures. There is also strong positive correlation between TOPSIS and WASPAS method results. This result confirms the stability of the analysis. Originality/value This paper contributes to sport performance management literature by using MCDM methods in FIFA World Cup 2018 teams. To the best of the authors’ knowledge, this is the first paper to measure performance of an international football organization via MCDM methods.