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This study presents a two-stage stochastic aggregate production planning model to determine the optimal renewable generation capacity, production plan, workforce levels, and machine hours that minimize a production system’s operational cost. The model considers various uncertainties, including demand for final products, machine and labor hours available, and renewable power supply. The goal is to evaluate the feasibility of decarbonizing the manufacturing, transportation, and warehousing operations by adopting onsite wind turbines and solar photovoltaics coupled with battery systems assuming the facilities are energy prosumers. First-stage decisions are the siting and sizing of wind and solar generation, battery capacity, production quantities, hours of labor to keep, hire, or layoff, and regular, overtime, and idle machine hours to allocate over the planning horizon. Second-stage recourse actions include storing products in inventory, subcontracting or backorder, purchasing or selling energy to the main grid, and daily charging or discharging energy in the batteries in response to variable generation. Climate analytics performed in San Francisco and Phoenix permit to derive capacity factors for the renewable energy technologies and test their implementation feasibility. Numerical experiments are presented for three instances: island microgrid without batteries, island microgrid with batteries, and grid-tied microgrid for energy prosumer. Results show favorable levelized costs of energy that are equal to USD48.37/MWh, USD64.91/MWh, and USD36.40/MWh, respectively. The model is relevant to manufacturing companies because it can accelerate the transition towards eco-friendly operations through distributed generation. 

A diagnostic of thirty questions administered to incoming STEM students in Fall 2013 and Fall 2015 - Fall 2018 reflects that their spatial visualization skills (SVS) need to be improved. Previous studies in the SVS subject [1], [2], [3] report that well-developed SVS skills lead to students’ success in Engineering and Technology, Computer Science, Chemistry, Computer Aided Design and Mathematics. Authors [4], [5] mention that aptitude in spatial skills is gradually becoming a standard assessment of an individual’s likelihood to succeed as an engineer. This research reports the qualitative and quantitative results of a project designed to improve SVS’s for STEM students managed under two strategies. The first strategy utilized was a series of face-to-face (FtF), two-hour training sessions taught over six weeks to all majors in STEM. This strategy was offered in Spring 2014 and every semester from Fall 2015 - Spring 2018. The second strategy was an embedded training (ET) implemented by one faculty from Fall 2017- Fall 2018. The faculty embedded the training in the US 1100 freshman seminar and was highly motivated to increase awareness of students on the importance and applicability of SVS in their fields of study. As reported by Swail et al. [6], cognitive, social, and institutional factors are key elements to best support students’ persistence and achievement. Both interventions used in this project encompassed all these factors and were supported by an NSF IUSE grant (2015-2019) to improve STEM retention. The FtF training was taken by 34 students majoring in diverse STEM fields. Its effectiveness was statistically assessed through a t-test to compare the results in the Purdue Spatial Visualization Skills Test - Rotations before and after the training and through analysis of surveys. Results were very positive; 85.29% of the participants improved their scores. The average change in scores was 5.29 (from 16.85 to 22.15; 17.65% improvement) and it was statistically significant (p-value 3.9E-8). On the surveys, 90% of students answered that they were satisfied with the training. Several students reported that they appreciated a connection between SVS, Calculus II and Engineering Graphics classes while others based the satisfaction on perceiving the critical role SVS will play in their careers. Results from the ET strategy were also encouraging. Teaching methods, curriculum and results are discussed in this paper. Adjustments to the teaching methods were done over 3 semesters. In the last semester, the faculty found that covering the modules at a slower pace than in the FtF training, asking the students to complete the pre-and post-diagnostic in class, and introducing the Spatial VisTM app to provide students with additional practice were key elements to assure students success and satisfaction. In conclusion, both strategies were demonstrated to be powerful interventions to increase students’ success because they not only offer students, particularly freshman, a way to refine SVS but also increase motivation in STEM while creating a community among students and faculty. The ET is effective and apt to be institutionalized. Lastly, this experimental research strengthens the literature on SVS. 

A variety of methods have been proposed to assist the integration of microgrid in flow shop systems with the goal of attaining eco-friendly operations. There is still a lack of integrated planning models in which renewable portfolio, microgrid capacity and production plan are jointly optimized under power demand and generation uncertainty. This paper aims to develop a two-stage, mixed-integer programming model to minimize the levelized cost of energy of a flow shop powered by onsite renewables. The first stage minimizes the annual energy use subject to a job throughput requirement. The second stage aims at sizing wind turbine, solar panels and battery units to meet the hourly electricity needs during a year. Climate analytics are employed to characterize the stochastic wind and solar capacity factor on an hourly basis. The model is tested in four locations with a wide range of climate conditions. Three managerial insights are derived from the numerical experiments. First, time-of-use tariff significantly stimulates the wind penetration in locations with medium or low wind speed. Second, regardless of the climate conditions, large-scale battery storage units are preferred under time-of-use rate but it is not the case under a net metering policy. Third, wind- and solar-based microgrid is scalable and capable of meeting short-term demand variation and long-term load growth with a stable energy cost rate. 

This research investigates on how extruder nozzle temperature, model infill rate (i.e. density) and number of shells affect the tensile strength of three-dimensional polylactic acid (PLA) products manufactured with the fused deposition model technology. Our goal is to enhance the quality of 3D printed products using the Makerbot Replicator. In the last thirty years, additive manufacturing has been increasingly commercialized, therefore, it is critical to understand properties of PLA products to broaden the use of 3D printing. We utilize a Universal Tensile Machine and Quality Engineering to comprehend tensile strength characteristics of PLA. Tensile strength tests are performed on PLA specimens to analyze their resistance to breakage. Statistical analysis of the experimental data collected shows that extruder temperature and model infill rate (i.e. density) affect tensile strength. 

This paper investigates the optimal design for a distributed generation (DG) system adopting wind turbines. The paper contribution is to formulate and solve a non-linear stochastic programming model to minimize the system lifecycle cost considering the loss-of-load probability and the thermal constraints using climate data from real settings. The model is solved in three cities representing high to medium to low wind speed profiles. Data analytics on 9-years hourly wind speed records permits to estimate the probability distribution for the power generation. The model is tested in a 9-node DG system with random loads. For a total mean load of 50.1 MW, New York requires the largest number of turbines at the highest annual cost of USD3,071,149, then Rio Gallegos is USD2,689,590, and Wellington is lowest with USD2,509,897. If the total load increases by 6 percent, the system is still capable to meet the reliability criteria but installed wind capacity and annual costs in New York and Rio Gallegos end higher than in Wellington. Results from decreasing the loss-of-load probability from 0.1 to 0.01 percentage show that the system designed using stochastic programming can be highly reliable. 

This work in progress is motivated by a self-study conducted at Texas State University. The study revealed that the average second year science, technology, engineering and math (STEM) student retention rate is 56% vs. 67% for all majors, and that 16% of STEM majors are female while 57% of all undergraduate students are female. Using these statistics, the authors identified the need to offer motivating experiences to freshman in STEM while creating a sense of community among other STEM students. This paper reports on the impact of two interventions designed by the authors and aligned with this need. The interventions are: (1) a one-day multi- disciplinary summer orientation (summer15) to give participants the opportunity to undertake projects that demonstrate the relevance of spatial and computational thinking skills and (2) a subsequent six-week spatial visualization skills training (fall 2015) for students in need to refine these skills. The interventions have spatial skills as a common topic and introduce participants to career applications through laboratory tours and talks. Swail et al.[1] mentions that the three elements to address in order to best support students’ persistence and achievement are cognitive, social, and institutional factors. The interventions address all elements to some extent and are part of an NSF IUSE grant (2015-2018) to improve STEM retention. The summer 2015 orientation was attended by 17 freshmen level students in Physics, Engineering, Engineering Technology, and Computer Science. The orientation was in addition to “Bobcat Preview”, a separate mandatory one-week length freshman orientation that includes academic advising and educational and spirit sessions to acclimate students to the campus. The effectiveness of the orientation was assessed through exit surveys administered to participants. Current results are encouraging; 100% of the participants answered that the orientation created a space to learn about science and engineering, facilitated them to make friends and encouraged peer interaction. Eighty percent indicated that the orientation helped them to build confidence in their majors. Exit survey findings were positively linked to a former exit survey from an orientation given to a group of 18 talented and low-income students in 2013. The training on refining spatial visualization skills connects to the summer orientation by its goals. It offers freshman students in need to refine spatial skills a further way to increase motivation to STEM and create community among other students. It is also an effective approach to support students’ persistence and achievement. Bairaktarova et al.[2] mention that spatial skills ability is gradually becoming a standard assessment of an individual’s likelihood to succeed as an engineer. Metz et al.[3] report that well-developed spatial skills have been shown to lead to success in Engineering and Technology, Computer Science, Chemistry, Computer Aided Design and Mathematics. The effectiveness of the fall 2015 training was assessed through comparison between pre and post tests results and exit surveys administered to participants. All participants improved their pre-training scores and average improvement in students’ scores was 18.334%.