Overview of environmental test plans for Space Station Freedom work package 4

The generation and distribution of electric power for Space Station Freedom (SSF) is critical to the station's success. Work Package 4 (WP-04) has the responsibility for the design, development, test, and delivery of the Electric Power System (EPS) for the SSF. During launch, assembly, and operation, the EPS will be subjected to various environments. A test and verification approach has been developed to assure that the EPS will function in these environments. An overview of that test program is presented with emphasis on environmental testing of hardware. Two key areas of the test program are highlighted in the overview. One area is the verification of the Solar Power Module (SPM) and associated cargo element hardware. This area includes detailing the plans for development and qualification testing of the SPM hardware. One series of tests, including modal and acoustic, has been completed on a development cargo element. Another area highlighted is the acceptance testing of high-power Orbital Replacement Units (ORU). The environmental test equipment plans are presented and reviewed in light of an aggressive production rate, which delivers ORU's to the WP-04 and other Space Station Work Packages. Through implementing the test program as outlined, the EPS hardware will be certified for flight and operation on the Space Station Freedom

Measuring The Effectiveness Of Retail Endcaps Using IoT Sensors And Machine Learning

In retail, an endcap is a display for products placed at the end of an aisle. An endcap is believed to give a brand a competitive advantage and is often leased at a premium price. This disclosure describes techniques to measure the effectiveness of retail endcaps by embedding internet-of-things (IoT) sensors, e.g., proximity sensors, within the endcap. Effectiveness is measured, for example, based on factors such as the amount of foot traffic, the amount of time customers dwell on the products displayed in the endcap, etc. Machine learning is used to discern customer behavioral patterns, such that the investment of the retailer in the endcaps is optimized

Some inequalities for kk-colored partition functions

Motivated by a partition inequality of Bessenrodt and Ono, we obtain analogous inequalities for $k$-colored partition functions $p_{-k}(n)$ for all $k\geq2$. This enables us to extend the $k$-colored partition function multiplicatively to a function on $k$-colored partitions, and characterize when it has a unique maximum. We conclude with one conjectural inequality that strengthens our results.Comment: 11 pages, 1 tabl