Defining self-sufficiency in the United States Marine Corps

The purpose of this project is to define self-sufficiency as it relates to the Marine Corps, identify the primary elements of self-sufficiency, and describe the interrelationships among these elements. Upon reviewing applicable literature, we have found three primary elements of self-sufficiency to be environment, time, and supply chain. Upon analyzing these elements and their interrelationships, we present a framework of self-sufficiency that (1) Marine Corps units can apply to internally increase their self-sufficiency, and (2) others can use to conduct further in-depth research on how to increase or optimize Marine Corps units self-sufficiency.http://archive.org/details/definingselfsuff1094538886Captain, United States Marine CorpsApproved for public release; distribution is unlimited

Computer Learning Behavior: Strategies for Learning and Behavior Improvement

A survey of 124 students in computer applications classes was conducted to determine students’ perceptions of computer learning in the following areas: promptness, listening, computer obsession, computer anxiety, anger, frustration, enjoyment of computers, importance of computer knowledge, and relative importance of computer skills and people skills. Results indicated that there were behavior problems involving promptness, listening, obsession, computer anxiety, anger, and frustration in the classroom. However, a majority of the students enjoyed working with computers. Additionally, they agreed that computer knowledge will be useful to them in the future; and most agreed that people skills and computer skills are equally important

Design of a TSR-based project learning strategy for biochemistry undergraduate teaching and research labs: a case study

Given the exponential growth of biochemical data and deep effect of computational methods on life sciences, there is a need to rethink undergraduate curricula. A project-oriented learning approach based on the Triangular Spatial Relationship (TSR) algorithm has been developed. The TSR-based method was designed for protein 3D structural comparison, motif discovery and probing molecular interactions. The uniqueness of the method benefits students’ learning of big data and computational methods. Specifically, students learn (i) how to search proteins of interest from the PDB archive, (ii) basic supercomputer skills, (iii) how to prepare datasets, (iv) how to perform protein structure and sequence analyses, (v) how to interpret the results, visualize protein structures and make graphs. Five specific strategies have been developed to achieve students’ highest potentials. (i) This lab exercise is designed as a project-oriented learning approach. (ii) The skills-first and concept-second approach is used. (iii) Students choose the proteins based on their interests. (iv) Students are encouraged to learn from each other to promote student–student interactions. (v) Students are required to write a report and/or present their studies. To assess students’ performance, we have developed an assessment rubric that includes (i) demonstration of supercomputer skills in job script preparation, submission and monitoring, (ii) skills in preparation of datasets, (iii) data analytical skills, (iv) project report, (v) presentation, and (vi) integration of the TSR-based method with other computational methods (e.g., molecular 3D structural visualization and protein sequence analysis). This project has been introduced in undergraduate biochemistry research and teaching labs for 4 years. Most students have learned the basic supercomputer skills as well as structure data analysis skills. Students’ feedback is positive and encouraging. It can be further developed as a module for an integrated computational chemistry lecture course

A New Robust Subspace Tracking Algorithm and its Application to Direction of Arrival Estimation and Tracking

International audienc

Maneuvering target tracking using q-learning based Kalman filter

International audienc