Search

Background Bacterial infections are common in postpartum dairy cows. Cortisol level has been observed to increase in dairy cows during peripartum period, and is associated with the endometrial innate immunity against pathogens like E.coli. However, the mechanism underlying how cortisol regulates E.coli-induced inflammatory response in bovine endometrial epithelial cells (BEEC) remains elusive. Results Cortisol decreased the expressions of IL1β, IL6, TNF-α, IL8, and TLR4 mRNA in BEEC treated with LPS or heat-killed E.coli, but up-regulated these gene expressions in BEEC stimulated by live E.coli. Conclusion Cortisol exerted the anti-inflammatory action on LPS- or heat-killed E.coli-stimulated BEEC, but the pro-inflammatory action on live E.coli-induced BEEC.

Worker assignment is a new type of problem in assembly line balancing problems, which typically occurs in sheltered work centers for the disabled. However, only a few contributions consider worker assignment in a two-sided assembly line. This research presents three variants of artificial bee colony algorithm to solve worker assignment and line balancing in two-sided assembly lines. The utilization of meta-heuristics is motivated by the NP-hard nature of the problem and the chosen methods utilize different operators for onlooker phase and scout phase. The proposed algorithms are tested on 156 cases generated from benchmark problems. A comparative study is conducted on the results obtained from the three proposed variants and other well-known metaheuristic algorithms, such as simulated annealing, particle swarm optimization and genetic algorithm. The computational study demonstrates that the proposed variants produce more promising results and are able to solve this new problem effectively in an acceptable computational time.

This paper focuses on implementing differential evolution (DE) to optimize the robotic assembly line balancing (RALB) problems with an objective of minimizing energy consumption in a straight robotic assembly line and thereby help to reduce energy costs. Few contributions are reported in literature addressing this problem. Assembly line balancing problems are classified as NP-hard, implying the need of using metaheuristics to solve realistic sized problems. In this paper, a well-known metaheuristic algorithm differential evolution is utilized to solve the problem. The proposed algorithm is tested on benchmark problems and the obtained results are compared with current state. It can be seen that the proposed DE algorithm is able to find a better solution for the considered objective function. Comparison of the computational time along with the cycle time is presented in detail.