Evaluation of Shoulder Stability During Forceful Arm Exertions

Shoulder musculoskeletal disorders (MSDs) are a major cause of morbidity and pain in the modern working population. Epidemiological literature suggests that forceful arm exertions pose an increased risk for shoulder MSD development. The majority of shoulder MSDs involve the glenohumeral joint. The glenohumeral joint is characteristically unstable and stabilized by concavity compression mechanism. In this study a biomechanical model of shoulder complex was used to examine the concavity compression mechanism. Mechanical loading of the glenohumeral joint during forceful arm exertions was analyzed to quantify the angular position of the resultant muscle force vector in 3D space. The resultant muscle force vectors were almost always directed anteriorly, medially, and inferiorly, independent of the magnitude and the direction of the external force application. The knowledge gained in this study could possibly be used to quantify strain imposed on the shoulder muscles during forceful arm exertions

Simulation-To-Flight (STF-1): A Mission to Enable CubeSat Software-Based Validation and Verification

The Simulation-to-Flight 1 (STF-1) CubeSat mission aims to demonstrate how legacy simulation technologies may be adapted for flexible and effective use on missions using the CubeSat platform. These technologies, named NASA Operational Simulator (NOS), have demonstrated significant value on several missions such as James Webb Space Telescope, Global Precipitation Measurement, Juno, and Deep Space Climate Observatory in the areas of software development, mission operations/training, verification and validation (V&V), test procedure development and software systems check-out. STF-1 will demonstrate a highly portable simulation and test platform that allows seamless transition of mission development artifacts to flight products. This environment will decrease development time of future CubeSat missions by lessening the dependency on hardware resources. In addition, through a partnership between NASA GSFC, the West Virginia Space Grant Consortium and West Virginia University, the STF-1 CubeSat will hosts payloads for three secondary objectives that aim to advance engineering and physical-science research in the areas of navigation systems of small satellites, provide useful data for understanding magnetosphere-ionosphere coupling and space weather, and verify the performance and durability of III-V Nitride-based materials

“Is Energy That Different from Labor?” Similarity in Determinants of Intensity for Auto Assembly Plants

This paper addresses the question “Is energy that different from labor?” from the perspective of efficiency. It presents a novel statistical analysis for the auto assembly industry in North America to examine the determinants of relative energy intensity, and contrasts this with a similar analysis of the determinants of another important factor of production, labor intensity. The data used combine two non-public sources of data previously used to separately study key performance indicators (KPIs) for energy and labor intensity. The study found these two KPIs are statistically correlated (the correlation coefficient is 0.67) and the relationship is one-to-one. The paper identifies 11 factors that may influence both energy and labor intensity KPIs. The study then contrasts which of the empirical factors the two KPIs’ share and how they differ. Two novel statistical methods, Huber estimators and Multiple M-estimators, combined with regularized algorithms, are identified as the preferred methods for robust statistical models to estimate energy intensity. Based on our analysis, the underlying determinants of energy efficiency and labor productivity are quite similar. This implies that strategies to improve energy may have spillover benefits to labor, and vice versa. The study shows vehicle variety, car model types, and launch of a new vehicle penalize both energy and labor intensity, while flexible manufacturing, production volume, and year of production improve both energy and labor intensity. In addition, the study found that the plants that produce small cars are more energy-efficient and productive compared to plants that produce large vehicles. Moreover, in a given functional unit, i.e., on a per-unit basis, Japanese plants are more energy-efficient and productive compared to American plants. Plant managers can use the proposed data-driven approach to make the right decisions about the energy efficiency targets and improve plants’ energy efficiency up to 38% using hybrid regression methods, mathematical modeling, plants’ resources, and constraints