Plants may not have optimum growth hormone production potential under suboptimal growth and environmental conditions. The exogenous application of microbes is a potential and economical source of plant hormones. A laboratory trial was performed to evaluate the hormone biosynthesis potential of several soil microbes. Rhizobium sp. (Rhizobium trifolii, Rhizobium etli, Rhizobium meliloti, Rhizobium ciceri, Azorhizobium caulinodans, Bradyrhizobium japonicum, Rhizobium vulgaris, and Rhizobium phaseoli) were isolated from root nodules of various legumes and were biochemically characterized. Absorption spectroscopy (535, 254 and 665 nm λ) was used to quantify indole acetic acid (IAA), gibberellin and cytokinin production of different Rhizobium species. B3 isolate of Rhizobium trifolii had yielded (14.54 and 21.37 µg mL-1) IAA equivalents without and with L-tryptophan (L-TRP) supplement, that was statistically at par with same species while significantly greater than other Rhizobium isolates. Gibberellin production was found statistically non-significant in all isolates. Rhizobium phaseoli (Vp1) yielded 1.68 µg mL-1 cytokinin that was at par with the same species isolates and with Rhizobium trifolii but was critically greater in amount than other species. It was concluded that Rhizobium plays a vital role in plant growth and development can produce auxin and gibberellins, but some could produce cytokinin. Keywords: Phytohormone, Biosynthesis, IAA, PGPR, Auxin, Gibberellins, Cytokinin

Soil erosion is common and has a wide range of spatiotemporal variability. It is crucial in determining sediment output, which is essential for proper watershed management. In this research, we propose morphometric deterministic models (MDM) for prediction of sediment yield index using morphometric parameters of 49 watersheds from Upper Narmada Basin of Madhya Pradesh state, India. For this purpose, Shuttle Radar Topography Mission generated Digital Elevation Model was used to extract and analyze 12 morphometric parameters including linear, aerial, and relief parameters. Principle Component Analysis has been applied for the most effective parameter estimation. The linear and nonlinear MDM were discovered to be suitable for the field of sediment research due to the high value of R2 (over 70%). The sediment yield forecasting is critical for taking the appropriate management measures in the watershed to reduce the sediment load in the reservoir and extend the life of the structure.

Breast cancer is the most common type of cancer worldwide. A robotic system performing autonomous breast palpation can make a significant impact on the related health sector worldwide. However, robot programming for breast palpating with different geometries is very complex and unsolved. Robot learning from demonstrations (LfD) reduces the programming time and cost. However, the available LfD are lacking the modelling of the manipulation path/trajectory as an explicit function of the visual sensory information. This paper presents a novel approach to manipulation path/trajectory planning called deep Movement Primitives that successfully generates the movements of a manipulator to reach a breast phantom and perform the palpation. We show the effectiveness of our approach by a series of real-robot experiments of reaching and palpating a breast phantom. The experimental results indicate our approach outperforms the state-of-the-art method.

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Two-level fractional factorial design is an efficient technique for experiments considering a large number of factors. To evaluate the efficiency and analyze the data for such a design, we need to know the generators for the design, so that, using the generators, we can generate its defining relation and alias structure. Although knowing the generators is important for a two-level fractional factorial design, it is not unusual in actual industrial situations for the generators used in the design to be lost or overlooked while the design is performed. Since Taguchi methods has been widely applied in industry, in this research, an efficient algorithm based on Taguchi orthogonal arrays (OA's) and interaction tables is developed to identify the generators for given designs. Furthermore, with the investigation of the insights of Taguchi OA's and interaction tables, this research may provide ideas for making Taguchi methods a simple tool for developing optimal designs for 2k - p experiments.