This study was conducted to evaluate the effect of rumen origin lactate-utilizing bacteria (LUB) as one of the potential treatments on subacute ruminal acidosis (SARA) during in vitro challenge compared to buffering agents (NaHCO3, sea minerals, MgO) and direct-fed microorganism (yeast). We hypothesized that rumen LUB (RLUB) could be a potential treatment to treat ruminal acidosis. The supplementation level of other treatments was determined by referring to previous studies in the literature. The 108 CFU/g freeze-dried RLUB isolated from Hanwoo cattle were compared with 0.1% NaHCO3, 0.8% of MgO, 0.5% sea mineral and 0.4% yeast during in vitro SARA challenge. Rumen fluid collected from one cannulated Holstein and one Hanwoo steer fed by maize silage was mixed with 0.5g feed consisted of 0.05g forage and 0.45g concentrate. These mixtures were incubated in triplication for 3, 6, 12 and 24h. After 6h of incubation, along with MgO and sea minerals, RLUB treatment showed higher (p<0.05) ㏗ values than control with no significant differences in total volatile fatty acid concentration. However, in the same period, the propionate concentration and A:P ratio were higher in RLUB than in the other treatment (p<0.05), which might alter the fermentation pattern. On the other hand, the RLUB treatment produced a higher (p<0.05) ammonia-N concentration. Based on these results, we can conclude that RLUB might have the potential to alleviate SARA. Nonetheless, further study on its mechanism in SARA is required, especially with live animals.

The optimal grasping point of the object varies depending on the shape of the object, such as the weight, the material, the grasping contact with the robot hand, and the grasping force. In order to derive the optimal grasping points for each object by a three fingered robot hand, optimal point and posture have been derived based on the geometry of the object and the hand using the artificial neural network. The optimal grasping cost function has been derived by constructing the cost function based on the probability density function of the normal distribution. Considering the characteristics of the object and the robot hand, the optimum height and width have been set to grasp the object by the robot hand. The resultant force between the contact area of the robot finger and the object has been estimated from the grasping force of the robot finger and the gravitational force of the object. In addition to these, the geometrical and gravitational center points of the object have been considered in obtaining the optimum grasping position of the robot finger and the object using the artificial neural network. To show the effectiveness of the proposed algorithm, the friction cone for the stable grasping operation has been modeled through the grasping experiments.