Abstract. An assessment of the economic, energy consumption, and greenhouse gas (GHG) emission dimensions of forest-based biomass harvest stage in the state of Michigan, U.S. through gathering data from literature, database, and other relevant sources, was performed. The assessment differentiates harvesting systems (cut-to-length harvesting, whole tree harvesting, and motor-manual harvesting), harvest types (30%, 70%, and 100% cut) and forest types (hardwoods, softwoods, mixed hardwood/softwood, and softwood plantations) that characterize Michigan's logging industry. Machine rate methods were employed to determine unit harvesting cost. A life cycle inventory was applied to calculating energy demand and GHG emissions of different harvesting scenarios, considering energy and material inputs (diesel, machinery, etc.) and outputs (emissions) for each process (cutting, forwarding/skidding, etc.). A sensitivity analysis was performed for selected input variables for the harvesting operation in order to explore their relative importance. The results indicated that productivity had the largest impact on harvesting cost followed by machinery purchase price, yearly scheduled hours, and expected utilization. Productivity and fuel use, as well as fuel factors, are the most influential environmental impacts of harvesting operations.

There are amounts of patients with locomotor dysfunction caused by stroke until now. Body weight supported treadmill training (BWSTT) has proved to be an efficient method of rehabilitation training for those people. The lower exoskeleton consists of two legs which is used to guide and assist motions of patients with the help of weight support devices and a treadmill. A prototype of the body weight support exoskeleton rehabilitation device (BWSERD) has been designed in this paper, which contains two pairs of direct drives at hip and knee joints. It has also four torque transducers and four encoders. In order to conduct the patients-passive rehabilitation training after stroke, a control strategy based on neuro network and sliding mode controller is developed. The effectiveness of the proposed method is confirmed by the simulation results.