A Microfluidic Method to Measure Small Molecule Diffusion in Hydrogels

Drug release from a fluid-contacting biomaterial is simulated using a microfluidic device with a channel defined by solute-loaded hydrogel; as water is pumped through the channel, solute transfers from the hydrogel into the water. Optical analysis of in-situ hydrogels, characterization of the microfluidic device effluent, and NMR methods were used to find diffusion coefficients of several dyes (model drugs) in poly( ethylene glycol) diacrylate (PEG-DA) hydrogels. Diffusion coefficients for methylene blue and sulforhodamine 101 in PEG-DA calculated using the three methods are in good agreement; both dyes are mobile in the hydrogel and elute from the hydrogel at the aqueous channel interface. However, the dye acid blue 22 deviates from typical diffusion behavior and does not release as expected from the hydrogel. Importantly, only the microfluidic method is capable of detecting this behavior. Characterizing solute diffusion with a combination of NMR, optical and effluent methods offer greater insight into molecular diffusion in hydrogels than employing each technique individually. The NMR method made precise measurements for solute diffusion in all cases. The microfluidic optical method was effective for visualizing diffusion of the optically active solutes. The optical and effluent methods show potential to be used to screen solutes to determine if they elute from a hydrogel in contact with flowing fluid. Our data suggest that when designing a drug delivery device, analyzing the diffusion from the molecular level to the device level is important to establish a complete picture of drug elution, and microfluidic methods to study such diffusion can play a key role. (C) 2013 Elsevier B.V. All rights reserved.

Development of the Heat and Energy Concept Inventory: Preliminary Results on the Prevalence and Persistence of Engineering Students' Misconceptions

BACKGROUND Students frequently hold a number of misconceptions related to temperature, heat and energy. There is not currently a concept inventory with sufficiently high internal reliability to assess these concept areas for research purposes. Consequently, there is little data on the prevalence of these misconceptions amongst undergraduate engineering students. PURPOSE (HYPOTHESIS) This work presents the Heat and Energy Concept Inventory (HECI) to assess prevalent misconceptions related to: (1) Temperature vs. Energy, (2) Temperature vs. Perceptions of Hot and Cold, (3) Factors that affect the Rate vs. Amount of Heat Transfer and (4) Thermal Radiation. The HECI is also used to document the prevalence of misconceptions amongst undergraduate engineering students. DESIGN/METHOD Item analysis, guided by classical test theory, was used to refine individual questions on the HECI. The HECI was used in a one group, pre-test-post-test design to assess the prevalence and persistence of targeted misconceptions amongst a population of undergraduate engineering students at diverse institutions. RESULTS Internal consistency reliability was assessed using Kuder-Richardson Formula 20; values were 0.85 for the entire instrument and ranged from 0.59 to 0.76 for the four subcategories of the HECI. Student performance on the HECI went from 49.2% to 54.5% after instruction. Gains on each of the individual subscales of the HECI, while generally statistically significant, were similarly modest. CONCLUSIONS The HECI provides sufficiently high estimates of internal consistency reliability to be used as a research tool to assess students' understanding of the targeted concepts. Use of the instrument demonstrates that student misconceptions are both prevalent and resistant to change through standard instruction.

Increasing Utilization of US Electric Grids via Smart Technologies: Integration of Load Management, Real Time Pricing, Renewables, EVs and Smart Grid Sensors

Electric power grids throughout the world suffer from serious inefficiencies associated with under-utilization due to demand patterns, engineering design and load following approaches in use today. These grids consume much of the world’s energy and represent a large carbon footprint. From material utilization perspectives significant hardware is manufactured and installed for this infrastructure often to be used at less than 20-40% of its operational capacity for most of its lifetime. These inefficiencies lead engineers to require additional grid support and conventional generation capacity additions when renewable technologies (such as solar and wind) and electric vehicles are to be added to the utility demand/supply mix. Using actual data from the PJM [PJM 2009] the work shows that consumer load management, real time price signals, sensors and intelligent demand/supply control offer a compelling path forward to increase the efficient utilization and carbon footprint reduction of the world’s grids. Underutilization factors from many distribution companies indicate that distribution feeders are often operated at only 70-80% of their peak capacity for a few hours per year, and on average are loaded to less than 30-40% of their capability. By creating strong societal connections between consumers and energy providers technology can radically change this situation. Intelligent deployment of smart sensors, smart electric vehicles, consumer-based load management technology very high saturations of intermittent renewable energy supplies can be effectively controlled and dispatched to increase the levels of utilization of existing utility distribution, substation, transmission, and generation equipment. The strengthening of these technology, society and consumer relationships requires rapid dissemination of knowledge (real time prices, costs & benefit sharing, demand response requirements) in order to incentivize behaviors that can increase the effective use of technological equipment that represents one of the largest capital assets modern society has created.

Wow! Linear systems and signal processing is fun!

We describe a recent offering of a linear systems and signal processing course for third-year electrical and computer engineering students. This course is a pre-requisite for our first digital signal processing course. Students have traditionally viewed linear systems courses as mathematical and extremely difficult. Without compromising the rigor of the required concepts, we strived to make the course fun, with application-based hands-on laboratory projects. These projects can be modified easily to meet specific instructors' preferences. © 2011 IEEE.(17 refs)

Exploring Engineering

Engineering faculty members at Bucknell University have established a course required for freshman engineering students and open to liberal arts students. The course has been designed to stimulate and enhance student interest in all the engineering disciplines at Bucknell. The course ranges broadly across small groups, faculty-lead recitations, laboratory experiences, student design projects, traditional lectures, and guest speakers. The exploring engineering course has completed its second year. The authors describe the course, the changes made since the initial offering and the impact on the students and faculty involved. They also present and interpret student evaluations of the course.(4 refs)

Corpus-Based Speech Enhancement With Uncertainty Modeling and Cepstral Smoothing

We present a new approach for corpus-based speech enhancement that significantly improves over a method published by Xiao and Nickel in 2010. Corpus-based enhancement systems do not merely filter an incoming noisy signal, but resynthesize its speech content via an inventory of pre-recorded clean signals. The goal of the procedure is to perceptually improve the sound of speech signals in background noise. The proposed new method modifies Xiao's method in four significant ways. Firstly, it employs a Gaussian mixture model (GMM) instead of a vector quantizer in the phoneme recognition front-end. Secondly, the state decoding of the recognition stage is supported with an uncertainty modeling technique. With the GMM and the uncertainty modeling it is possible to eliminate the need for noise dependent system training. Thirdly, the post-processing of the original method via sinusoidal modeling is replaced with a powerful cepstral smoothing operation. And lastly, due to the improvements of these modifications, it is possible to extend the operational bandwidth of the procedure from 4 kHz to 8 kHz. The performance of the proposed method was evaluated across different noise types and different signal-to-noise ratios. The new method was able to significantly outperform traditional methods, including the one by Xiao and Nickel, in terms of PESQ scores and other objective quality measures. Results of subjective CMOS tests over a smaller set of test samples support our claims.

Estimates of Use of Research-Based Instructional Strategies in Core Electrical or Computer Engineering Courses

Many research-based instruction strategies (RBISs) have been developed; their superior efficacy with respect to student learning has been demonstrated in many studies. Collecting and interpreting evidence about: 1) the extent to which electrical and computer engineering (ECE) faculty members are using RBISs in core, required engineering science courses, and 2) concerns that they express about using them, are important aspects of understanding how engineering education is evolving. The authors surveyed ECE faculty members, asking about their awareness and use of selected RBISs. The survey also asked what concerns ECE faculty members had about using RBISs. Respondent data showed that awareness of RBISs was very high, but estimates of use of RBISs, based on survey data, varied from 10% to 70%, depending on characteristics of the strategy. The most significant concern was the amount of class time that using an RBIS might take; efforts to increase use of RBISs must address this.

Identifying Resiliency Performance Measures for Megaregional Planning Case Study of the Transportation Corridor Between Boston, Massachusetts, and Washington, DC

Transportation corridors in megaregions present a unique challenge for planners because of the high concentration of development, complex interjurisdictional issues, and history of independent development of core urban centers. The concept of resilience, as applied to megaregions, can be used to understand better the performance of these corridors. Resiliency is the ability to recover from or adjust easily to change. Resiliency performance measures can be expanded on for application to megaregions throughout the United States. When applied to transportation corridors in megaregions and represented by performance measures such as redundancy, continuity, connectivity, and travel time reliability, the concept of resiliency captures the spatial and temporal relationships between the attributes of a corridor, a network, and neighboring facilities over time at the regional and local levels. This paper focuses on the development of performance measurements for evaluating corridor resiliency as well as a plan for implementing analysis methods at the jurisdictional level. The transportation corridor between Boston, Massachusetts, and Washington, D.C., is used as a case study to represent the applicability of these measures to megaregions throughout the country.