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The potential of near infrared spectroscopy (NIRS) for the environmental biomonitoring of plants
(2019)
In the current environmental condition, the increase in pollution of the air, water, and soil indirectly will induce plants stress and decrease vegetation growth rate. These issues pay more attention to be solved by scientists worldwide. The higher level of chemical pollutants also induced the gradual changes in plants metabolism and decreased enzymatic activity. Importantly, environmental biomonitoring may play a pivotal contribution to prevent biodiversity degradation and plants stress due to pollutant exposure. Several previous studies have been done to monitor the effect of environmental changes on plants growth. Among that, Near Infrared spectroscopy (NIRS) offers an alternative way to observe the significant alteration of plant physiology caused by environmental damage related to pollution. Impairment of photosynthesis, nutrient and oxidative imbalances, and mutagenesis.
Manufacturing Process Simulation for the Prediction of Tool-Part-Interaction and Ply Wrinkling
(2019)
Effective training requires high muscle forces potentially leading to training-induced injuries. Thus, continuous monitoring and controlling of the loadings applied to the musculoskeletal system along the motion trajectory is required. In this paper, a norm-optimal iterative learning control algorithm for the robot-assisted training is developed. The algorithm aims at minimizing the external knee joint moment, which is commonly used to quantify the loading of the medial compartment. To estimate the external knee joint moment, a musculoskeletal lower extremity model is implemented in OpenSim and coupled with a model of an industrial robot and a force plate mounted at its end-effector. The algorithm is tested in simulation for patients with varus, normal and valgus alignment of the knee. The results show that the algorithm is able to minimize the external knee joint moment in all three cases and converges after less than seven iterations.