Upon graduation, engineers entering the workforce are not always trained to work in a collaborative environment where a detailed understanding of common business, project management, and leadership skills may be required. In order to create a paradigm shift in engineering education, where students' capacities are pushed beyond their limits in order to redefine what an engineer is and develop these skill sets, Engineering Leadership (E-Lead) students at The University of Texas at El Paso have taken ownership of not only their own education, but the education of future students.In order to develop students as leaders, the current Introduction to Engineering Leadership course has been developed and taught by second year Engineering Leadership students. Second year students were placed in educator/mentor positions in order to develop their leadership skills. The purpose of the course was not only to give second year students a leadership opportunity and an understanding of the importance of guiding people, but also to introduce a unique culture being created in the Engineering Leadership program and provide leadership models for incoming students to learn from second year students.These second year students, also called Mavericks, worked closely with Engineering Leadership faculty, as well as faculty from Franklin W. Olin College of Engineering (Needham, MA), throughout the summer in order to develop curriculum for the incoming cohort of students in the fall of 2014. The goal of the course was to create an immersive learning environment that was also social, relatable, and inspiring to the instructors and the students. In order to achieve that goal, the Mavericks were given the opportunity to teach the course. The curriculum developed by the students was created to focus on three major disciplines: leadership identity development, innovative thinking, and hands on skills. These disciplines were taught in a studio environment through group discussions and interactive individual and group projects.This redesign effort by students not only resulted in a refined curriculum for the E-Lead program, but also improved the course by increasing the feeling of community for incoming students and thereby increased retention in the course from 60% to 92% (measured by the ratio of students that completed the course to those enrolled as of census day). More importantly, this experience of being placed in the curriculum development driver seat, also served to help the Mavericks redefine leadership, gain a better understanding of leadership, and increase their leadership skills (4.5, STDV 0.55; 4.67, STDV 0.52; 4.67, STDV 0.52; based on an ordinal scale with 1 being strongly disagree and 5 being strongly agree). The experience also helped them increase their Character, Competence, and Capacity (4.67, STDV 0.52; 4.33, STDV 0.82; 4.92, STDV 0.20). The Mavericks also agreed that the experience helped them increase their innovative problem solving and thinking skills (4.17, STD 0.41) and develop their identity (4.25, STDV 0.76)...
Healthcare service centers must be sited in strategic locations that meet the immediate needs of patients. The current situation due to the COVID-19 pandemic makes this problem particularly relevant. Assume that each center corresponds to an assigned place for vaccination and that each center uses one or more vaccine brands/laboratories. Then, each patient could choose a center instead of another, because she/he may prefer the vaccine from a more reliable laboratory. This defines an order of preference that might depend on each patient who may not want to be vaccinated in a center where there are only her/his non-preferred vaccine brands. In countries where the vaccination process is considered successful, the order assigned by each patient to the vaccination centers is defined by incentives that local governments give to their population. These same incentives for foreign citizens are seen as a strategic decision to generate income from tourism. The simple plant/center location problem (SPLP) is a combinatorial approach that has been extensively studied. However, a less-known natural extension of it with order (SPLPO) has not been explored in the same depth. In this case, the size of the instances that can be solved is limited. The SPLPO considers an order of preference that patients have over a set of facilities to meet their demands. This order adds a new set of constraints in its formulation that increases the complexity of the problem to obtain an optimal solution. In this paper, we propose a new two-stage stochastic formulation for the SPLPO (2S-SPLPO) that mimics the mentioned pandemic situation, where the order of preference is treated as a random vector. We carry out computational experiments on simulated 2S-SPLPO instances to evaluate the performance of the new proposal. We apply an algorithm based on Lagrangian relaxation that has been shown to be efficient for large instances of the SPLPO. A potential application of this new algorithm to COVID-19 vaccination is discussed and explored based on sensor-related data. Two further algorithms are proposed to store the patient’s records in a data warehouse and generate 2S-SPLPO instances using sensors.
Governments must consider different issues when deciding on the location of healthcare centers. In addition to the costs of opening such centers, three further elements should be addressed: accessibility, demand, and equity. Such locations must be chosen to meet the corresponding demand, so that they guarantee a socially equitable distribution, and to ensure that they are accessible to a sufficient degree. The location of the centers must be chosen from a set of possible facilities to guarantee certain minimum standards for the operational viability of the centers. Since the set of potential locations does not necessarily cover the demand of all geographical zones, the efficiency criterion must be maximized. However, the efficient distribution of resources does not necessarily meet the equity criterion. Thus, decision-makers must consider the trade-off between these two criteria: efficiency and equity. The described problem corresponds to the challenge that governments face in seeking to minimize the impact of the pandemic on citizens, where healthcare centers may be either public hospitals that care for COVID-19 patients or vaccination points. In this paper, we focus on the problem of a zone-divided region requiring the localization of healthcare centers. We propose a non-linear programming model to solve this problem based on a coverage formula using the Gini index to measure equity and accessibility. Then, we consider an approach using epsilon constraints that makes this problem solvable with mixed integer linear computations at each iteration. A simulation algorithm is also considered to generate problem instances, while computational experiments are carried out to show the potential use of the proposed mathematical programming model. The results show that the spatial distribution influences the coverage level of the healthcare system. Nevertheless, this distribution does not reduce inequity at accessible healthcare centers, as the distribution of the supply of health centers must be incorporated into the decision-making process.
The design and construction of a hybrid prototype for drying algal biomass is presented. The dryer is composed from a solar induced air collector and a greenhouse drying chamber which main energy source is that one coming from the sun. It is complemented with an electric system used as auxiliary heating mechanism. During the data collection, meteorological conditions (radiation, temperature, local humidity) were measured in order to determine the effect on the final quality of biomass going to be used. The drying productivity is analyzed in function of the solar energy received and the product moisture. The drying rate curves of the selected samples, finally, curves of behavior variation of the temperature profile were elaborated;. Useful energy, theoretical and experimental losses of the system were obtained and during the development of the project
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