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1. Life cycle based alternatives assessment (LCAA) for chemical substitution

   https://doi.org/10.1039/d0gc01544j  

   Fantke, Peter ; Huang, Lei ; Overcash, Michael ; Griffing, Evan ; Jolliet, Olivier ( September 2020 , Green Chemistry)

   null (Ed.)

   The world faces an increasing need to phase out harmful chemicals and design sustainable alternatives across various consumer products and industrial applications. Alternatives assessment is an emerging field with focus on identifying viable solutions to substitute harmful chemicals. However, current methods fail to consider trade-offs from human and ecosystem exposures, and from impacts associated with chemical supply chains and product life cycles. To close this gap, we propose a life cycle based alternatives assessment (LCAA) framework for consistently integrating quantitative exposure and life cycle impact performance in the substitution process. We start with a pre-screening based on function-related decision rules, followed by three progressive tiers from (1) rapid risk screening of various alternatives for the consumer use stage, to (2) an assessment of chemical supply chain impacts for selected alternatives with substantially different synthesis routes, and (3) an assessment of product life cycle impacts for alternatives with substantially different product life cycles. Each tier focuses on relevant impacts and uses streamlined assessment methods. While the initial risk screening will be sufficient for evaluating chemicals with similar supply chains, each additional tier helps further restricting the number of viable solutions, while avoiding unacceptable trade-offs. We test our LCAA framework in a proof-of-concept case study for identifying suitable alternatives to a harmful plasticizer in household flooring. Results show that the use stage dominates human health impacts across alternatives, supporting that a rapid risk screening is sufficient unless very different supply chains or a broader set of alternative materials or technologies are considered. Combined with currently used indicators for technical and economic performance, our LCAA framework is suitable for informing function-based substitution at the level of chemicals, materials and product applications to foster green and sustainable chemistry solutions. 

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2. Board 321: Integrating Design Thinking and Digital Fabrication into Engineering Technology Education through Interdisciplinary Professional Learning

   Russell, C. ( June 2023 , ASEE annual conference exposition proceedings)

   In Northern Virginia, engineering technology career pathways are underdeveloped. Rapid changes in industrial processes have led to an increased need for adaptable and flexible workers who can respond creatively to shifting production technologies (Agarwal et al., 2018). In particular, workers with expertise in design thinking, communication, and critical thinking skills are in high demand (Giffi et al., 2018). Despite high wages created by this demand, engineering technology careers are largely invisible to students and the belief that manufacturing is low-tech persists (Giffi et al., 2017; Magnolia Consulting, 2022). These conditions suggest that investment in teacher professional learning (PL) is warranted. The integration of digital fabrication (e.g., 3D printing) into classroom teaching is one promising avenue to increase student interest and awareness of engineering technology careers (Peppler et al., 2016). However, studies of classroom implementation of digital fabrication technologies also report that teachers struggle to move beyond “keychain syndrome” – the tendency to fall back to reproducing simple objects, such as a keychain (Blikstein, 2014; Eisenberg, 2013). Educator PL in digital fabrication has centered on machine operation, and not the pedagogy, cognitive strategies, and processes to situate the technology (Smith et al., 2015). This project investigates the effectiveness of a sustained and interdisciplinary design thinking PL fellowship (“Makers By Design”) in improving integration of fabrication and design thinking into teaching practice. Design thinking is a non-linear user-centered strategy used to approach the design of products, emphasizing collaborative project-based methods to solve real-life problems (Brown, 2008). In the classroom, design thinking can serve as a cognitive bridge between a design problem and digital fabrication technologies. Participating Makers By Design fellows (n=17) completed 1) a series of design thinking workshops, 2) practice teaching at digital fabrication summer camps, and 3) development and integration of a design thinking challenge. Fellows completed the above in interdisciplinary groups consisting of K-12 teachers, college faculty, and librarians. Using a mixed-methods approach, this paper evaluates the extent to which participating educators reported increased confidence in integrating design thinking and digital fabrication into their instruction, demonstrated content mastery during teaching practice, and successfully developed and deployed design challenges. Data sources include pre- and post-surveys, focus groups within teaching discipline, and observations of summer camp and classroom teaching. Project results are aligned with the existing literature on successful PL (e.g., Capps et al., 2012) and recommendations for future digital fabrication-centered PL are discussed. 

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3. Embedding properties of network realizations of dissipative reduced order models

   Druskin, Guddati ( July 2022 , HOUSEHOLDER SYMPOSIUM XXI)

   Embedding properties of network realizations of dissipative reduced order models Jörn Zimmerling, Mikhail Zaslavsky,Rob Remis, Shasri Moskow, Alexander Mamonov, Murthy Guddati, Vladimir Druskin, and Liliana Borcea Mathematical Sciences Department, Worcester Polytechnic Institute https://www.wpi.edu/people/vdruskin Abstract Realizations of reduced order models of passive SISO or MIMO LTI problems can be transformed to tridiagonal and block-tridiagonal forms, respectively, via dierent modications of the Lanczos algorithm. Generally, such realizations can be interpreted as ladder resistor-capacitor-inductor (RCL) networks. They gave rise to network syntheses in the rst half of the 20th century that was at the base of modern electronics design and consecutively to MOR that tremendously impacted many areas of engineering (electrical, mechanical, aerospace, etc.) by enabling ecient compression of the underlining dynamical systems. In his seminal 1950s works Krein realized that in addition to their compressing properties, network realizations can be used to embed the data back into the state space of the underlying continuum problems. In more recent works of the authors Krein's ideas gave rise to so-called nite-dierence Gaussian quadrature rules (FDGQR), allowing to approximately map the ROM state-space representation to its full order continuum counterpart on a judicially chosen grid. Thus, the state variables can be accessed directly from the transfer function without solving the full problem and even explicit knowledge of the PDE coecients in the interior, i.e., the FDGQR directly learns" the problem from its transfer function. This embedding property found applications in PDE solvers, inverse problems and unsupervised machine learning. Here we show a generalization of this approach to dissipative PDE problems, e.g., electromagnetic and acoustic wave propagation in lossy dispersive media. Potential applications include solution of inverse scattering problems in dispersive media, such as seismic exploration, radars and sonars. To x the idea, we consider a passive irreducible SISO ROM fn(s) = Xn j=1 yi s + σj , (62) assuming that all complex terms in (62) come in conjugate pairs. We will seek ladder realization of (62) as rjuj + vj − vj−1 = −shˆjuj , uj+1 − uj + ˆrj vj = −shj vj , (63) for j = 0, . . . , n with boundary conditions un+1 = 0, v1 = −1, and 4n real parameters hi, hˆi, ri and rˆi, i = 1, . . . , n, that can be considered, respectively, as the equivalent discrete inductances, capacitors and also primary and dual conductors. Alternatively, they can be viewed as respectively masses, spring stiness, primary and dual dampers of a mechanical string. Reordering variables would bring (63) into tridiagonal form, so from the spectral measure given by (62 ) the coecients of (63) can be obtained via a non-symmetric Lanczos algorithm written in J-symmetric form and fn(s) can be equivalently computed as fn(s) = u1. The cases considered in the original FDGQR correspond to either (i) real y, θ or (ii) real y and imaginary θ. Both cases are covered by the Stieltjes theorem, that yields in case (i) real positive h, hˆ and trivial r, rˆ, and in case (ii) real positive h,r and trivial hˆ,rˆ. This result allowed us a simple interpretation of (62) as the staggered nite-dierence approximation of the underlying PDE problem [2]. For PDEs in more than one variables (including topologically rich data-manifolds), a nite-dierence interpretation is obtained via a MIMO extensions in block form, e.g., [4, 3]. The main diculty of extending this approach to general passive problems is that the Stieltjes theory is no longer applicable. Moreover, the tridiagonal realization of a passive ROM transfer function (62) via the ladder network (63) cannot always be obtained in port-Hamiltonian form, i.e., the equivalent primary and dual conductors may change sign [1]. 100 Embedding of the Stieltjes problems, e.g., the case (i) was done by mapping h and hˆ into values of acoustic (or electromagnetic) impedance at grid cells, that required a special coordinate stretching (known as travel time coordinate transform) for continuous problems. Likewise, to circumvent possible non-positivity of conductors for the non-Stieltjes case, we introduce an additional complex s-dependent coordinate stretching, vanishing as s → ∞ [1]. This stretching applied in the discrete setting induces a diagonal factorization, removes oscillating coecients, and leads to an accurate embedding for moderate variations of the coecients of the continuum problems, i.e., it maps discrete coecients onto the values of their continuum counterparts. Not only does this embedding yields an approximate linear algebraic algorithm for the solution of the inverse problems for dissipative PDEs, it also leads to new insight into the properties of their ROM realizations. We will also discuss another approach to embedding, based on Krein-Nudelman theory [5], that results in special data-driven adaptive grids. References [1] Borcea, Liliana and Druskin, Vladimir and Zimmerling, Jörn, A reduced order model approach to inverse scattering in lossy layered media, Journal of Scientic Computing, V. 89, N1, pp. 136,2021 [2] Druskin, Vladimir and Knizhnerman, Leonid, Gaussian spectral rules for the three-point second dierences: I. A two-point positive denite problem in a semi-innite domain, SIAM Journal on Numerical Analysis, V. 37, N 2, pp.403422, 1999 [3] Druskin, Vladimir and Mamonov, Alexander V and Zaslavsky, Mikhail, Distance preserving model order reduction of graph-Laplacians and cluster analysis, Druskin, Vladimir and Mamonov, Alexander V and Zaslavsky, Mikhail, Journal of Scientic Computing, V. 90, N 1, pp 130, 2022 [4] Druskin, Vladimir and Moskow, Shari and Zaslavsky, Mikhail LippmannSchwingerLanczos algorithm for inverse scattering problems, Inverse Problems, V. 37, N. 7, 2021, [5] Mark Adolfovich Nudelman The Krein String and Characteristic Functions of Maximal Dissipative Operators, Journal of Mathematical Sciences, 2004, V 124, pp 49184934 Go back to Plenary Speakers Go back to Speakers Go back 

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4. Experiences during the implementation of two different project-based learning assignments in a fluid mechanics course.

   Ayala, O. ( August 2022 , Zone 1 Conference of the American Society for Engineering Education)

   There is growing evidence of the effectiveness of project-based learning (PBL) in preparing students to solve complex problems. In PBL implementations in engineering, students are treated as professional engineers facing projects centered around real-world problems, including the complexity and uncertainty that influence such problems. Not only does this help students to analyze and solve an authentic real-world task, promoting critical thinking, but also students learn from each other, learning valuable communication and teamwork skills. Faculty play an important part by assuming non-conventional roles (e.g., client, senior professional engineer, consultant) to help students throughout this instructional and learning approach. Typically in PBLs, students work on projects over extended periods of time that culminate in realistic products or presentations. In order to be successful, students need to learn how to frame a problem, identify stakeholders and their requirements, design and select concepts, test them, and so on. Two different implementations of PBL projects in a fluid mechanics course are presented in this paper. This required, junior-level course has been taught since 2014 by the same instructor. The first PBL project presented is a complete design of pumped pipeline systems for a hypothetical plant. In the second project, engineering students partnered with pre-service teachers to design and teach an elementary school lesson on fluid mechanics concepts. With the PBL implementations, it is expected that students: 1) engage in a deeper learning process where concepts can be reemphasized, and students can realize applicability; 2) develop and practice teamwork skills; 3) learn and practice how to communicate effectively to peers and to those from other fields; and 4) increase their confidence working on open-ended situations and problems. The goal of this paper is to present the experiences of the authors with both PBL implementations. It explains how the projects were scaffolded through the entire semester, including how the sequence of course content was modified, how team dynamics were monitored, the faculty roles, and the end products and presentations. Students' experiences are also presented. To evaluate and compare students’ learning and satisfaction with the team experience between the two PBL implementations, a shortened version of the NCEES FE exam and the Comprehensive Assessment of Team Member Effectiveness (CATME) survey were utilized. Students completed the FE exam during the first week and then again during the last week of the semester in order to assess students’ growth in fluid mechanics knowledge. The CATME survey was completed mid-semester to help faculty identify and address problems within team dynamics, and at the end of the semester to evaluate individual students’ teamwork performance. The results showed that no major differences were observed in terms of the learned fluid mechanics content, however, the data showed interesting preliminary observations regarding teamwork satisfaction. Through reflective assignments (e.g., short answer reflections, focus groups), student perceptions of the PBL implementations are discussed in the paper. Finally, some of the challenges and lessons learned from implementing both projects multiple times, as well as access to some of the PBL course materials and assignments will be provided. 

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5. The Deep Learning Epilepsy Detection Challenge: Design, Implementation, and Test of a New Crowd-Sourced AI Challenge Ecosystem

   Kiral, Isabell ; Roy, Subhrajit ; Mummert, Todd ; Braz, Alan ; Tsay, Jason ; Tang, Jianbin ; Asif, Umar ; Schaffter, Thomas ; Mehmet, Eren ; Picone, Joseph ; et al ( April 2019 , Challenges in Machine Learning Competitions for All (CiML))

   null (Ed.)

   The DeepLearningEpilepsyDetectionChallenge: design, implementation, andtestofanewcrowd-sourced AIchallengeecosystem Isabell Kiral*, Subhrajit Roy*, Todd Mummert*, Alan Braz*, Jason Tsay, Jianbin Tang, Umar Asif, Thomas Schaffter, Eren Mehmet, The IBM Epilepsy Consortium◊ , Joseph Picone, Iyad Obeid, Bruno De Assis Marques, Stefan Maetschke, Rania Khalaf†, Michal Rosen-Zvi† , Gustavo Stolovitzky† , Mahtab Mirmomeni† , Stefan Harrer† * These authors contributed equally to this work † Corresponding authors: rkhalaf@us.ibm.com, rosen@il.ibm.com, gustavo@us.ibm.com, mahtabm@au1.ibm.com, sharrer@au.ibm.com ◊ Members of the IBM Epilepsy Consortium are listed in the Acknowledgements section J. Picone and I. Obeid are with Temple University, USA. T. Schaffter is with Sage Bionetworks, USA. E. Mehmet is with the University of Illinois at Urbana-Champaign, USA. All other authors are with IBM Research in USA, Israel and Australia. Introduction This decade has seen an ever-growing number of scientific fields benefitting from the advances in machine learning technology and tooling. More recently, this trend reached the medical domain, with applications reaching from cancer diagnosis [1] to the development of brain-machine-interfaces [2]. While Kaggle has pioneered the crowd-sourcing of machine learning challenges to incentivise data scientists from around the world to advance algorithm and model design, the increasing complexity of problem statements demands of participants to be expert data scientists, deeply knowledgeable in at least one other scientific domain, and competent software engineers with access to large compute resources. People who match this description are few and far between, unfortunately leading to a shrinking pool of possible participants and a loss of experts dedicating their time to solving important problems. Participation is even further restricted in the context of any challenge run on confidential use cases or with sensitive data. Recently, we designed and ran a deep learning challenge to crowd-source the development of an automated labelling system for brain recordings, aiming to advance epilepsy research. A focus of this challenge, run internally in IBM, was the development of a platform that lowers the barrier of entry and therefore mitigates the risk of excluding interested parties from participating. The challenge: enabling wide participation With the goal to run a challenge that mobilises the largest possible pool of participants from IBM (global), we designed a use case around previous work in epileptic seizure prediction [3]. In this “Deep Learning Epilepsy Detection Challenge”, participants were asked to develop an automatic labelling system to reduce the time a clinician would need to diagnose patients with epilepsy. Labelled training and blind validation data for the challenge were generously provided by Temple University Hospital (TUH) [4]. TUH also devised a novel scoring metric for the detection of seizures that was used as basis for algorithm evaluation [5]. In order to provide an experience with a low barrier of entry, we designed a generalisable challenge platform under the following principles: 1. No participant should need to have in-depth knowledge of the specific domain. (i.e. no participant should need to be a neuroscientist or epileptologist.) 2. No participant should need to be an expert data scientist. 3. No participant should need more than basic programming knowledge. (i.e. no participant should need to learn how to process fringe data formats and stream data efficiently.) 4. No participant should need to provide their own computing resources. In addition to the above, our platform should further • guide participants through the entire process from sign-up to model submission, • facilitate collaboration, and • provide instant feedback to the participants through data visualisation and intermediate online leaderboards. The platform The architecture of the platform that was designed and developed is shown in Figure 1. The entire system consists of a number of interacting components. (1) A web portal serves as the entry point to challenge participation, providing challenge information, such as timelines and challenge rules, and scientific background. The portal also facilitated the formation of teams and provided participants with an intermediate leaderboard of submitted results and a final leaderboard at the end of the challenge. (2) IBM Watson Studio [6] is the umbrella term for a number of services offered by IBM. Upon creation of a user account through the web portal, an IBM Watson Studio account was automatically created for each participant that allowed users access to IBM's Data Science Experience (DSX), the analytics engine Watson Machine Learning (WML), and IBM's Cloud Object Storage (COS) [7], all of which will be described in more detail in further sections. (3) The user interface and starter kit were hosted on IBM's Data Science Experience platform (DSX) and formed the main component for designing and testing models during the challenge. DSX allows for real-time collaboration on shared notebooks between team members. A starter kit in the form of a Python notebook, supporting the popular deep learning libraries TensorFLow [8] and PyTorch [9], was provided to all teams to guide them through the challenge process. Upon instantiation, the starter kit loaded necessary python libraries and custom functions for the invisible integration with COS and WML. In dedicated spots in the notebook, participants could write custom pre-processing code, machine learning models, and post-processing algorithms. The starter kit provided instant feedback about participants' custom routines through data visualisations. Using the notebook only, teams were able to run the code on WML, making use of a compute cluster of IBM's resources. The starter kit also enabled submission of the final code to a data storage to which only the challenge team had access. (4) Watson Machine Learning provided access to shared compute resources (GPUs). Code was bundled up automatically in the starter kit and deployed to and run on WML. WML in turn had access to shared storage from which it requested recorded data and to which it stored the participant's code and trained models. (5) IBM's Cloud Object Storage held the data for this challenge. Using the starter kit, participants could investigate their results as well as data samples in order to better design custom algorithms. (6) Utility Functions were loaded into the starter kit at instantiation. This set of functions included code to pre-process data into a more common format, to optimise streaming through the use of the NutsFlow and NutsML libraries [10], and to provide seamless access to the all IBM services used. Not captured in the diagram is the final code evaluation, which was conducted in an automated way as soon as code was submitted though the starter kit, minimising the burden on the challenge organising team. Figure 1: High-level architecture of the challenge platform Measuring success The competitive phase of the "Deep Learning Epilepsy Detection Challenge" ran for 6 months. Twenty-five teams, with a total number of 87 scientists and software engineers from 14 global locations participated. All participants made use of the starter kit we provided and ran algorithms on IBM's infrastructure WML. Seven teams persisted until the end of the challenge and submitted final solutions. The best performing solutions reached seizure detection performances which allow to reduce hundred-fold the time eliptologists need to annotate continuous EEG recordings. Thus, we expect the developed algorithms to aid in the diagnosis of epilepsy by significantly shortening manual labelling time. Detailed results are currently in preparation for publication. Equally important to solving the scientific challenge, however, was to understand whether we managed to encourage participation from non-expert data scientists. Figure 2: Primary occupation as reported by challenge participants Out of the 40 participants for whom we have occupational information, 23 reported Data Science or AI as their main job description, 11 reported being a Software Engineer, and 2 people had expertise in Neuroscience. Figure 2 shows that participants had a variety of specialisations, including some that are in no way related to data science, software engineering, or neuroscience. No participant had deep knowledge and experience in data science, software engineering and neuroscience. Conclusion Given the growing complexity of data science problems and increasing dataset sizes, in order to solve these problems, it is imperative to enable collaboration between people with differences in expertise with a focus on inclusiveness and having a low barrier of entry. We designed, implemented, and tested a challenge platform to address exactly this. Using our platform, we ran a deep-learning challenge for epileptic seizure detection. 87 IBM employees from several business units including but not limited to IBM Research with a variety of skills, including sales and design, participated in this highly technical challenge. 

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