The Center for Energy and Environmental Sustainability (CEES) was established in October 2010 with $5M in funding from NSF. CEES Phase II award from NSF started on October 1, 2019. CEES personnel include eight faculty members, 1 program coordinator, and 4 research staff members. CEES is addressing the challenging interdisciplinary problems and is paving the way for scientific and technological breakthroughs in energy engineering to meet short- and long-term national needs. The Center’s research mission is accomplished through 3 distinct research thrust areas and strong education and outreach programs. The research areas include Bio-Energy (Energy crop cultivation and gasoline/diesel-like hydrocarbon fuel production from energy crops), Offshore Wind Energy (Massive offshore power generation and risk analysis with big data), and Environmental Sustainability (Produced water treatment and environmental impact evaluation with life cycle assessment). The Center’s education and outreach activities are carried through the Energy Engineering and Research Learning Initiative (EERLI), which includes a myriad of activities that engage K-12 students through graduate students. The researchers, including our students, have published 1 book, 185 peer-reviewed publications and have given more than 220 presentations at regional, national, and international conferences. CEES faculty and staff are also supported by other leveraged funding of about $12.82 M from NSF, USDA, Tran-SET, and industrial partners. CEES has supported more than 220 students, including 73 graduate, and 145 undergraduate students. The Center also established state-of-the-art research laboratories with high-quality equipment using leveraged funds.

Center for Energy & Environmental Sustainability (CEES)

Biomass is an organic material that normally comes from plants or animals. The discovery of converting biomass wastes into graphene is a successful route for recovering sources because it is sustainable, renewable, and abundant. For both basic research and practical applications, finding a novel way to produce graphene with a high yield and excellent crystalline structure is critical. For this research, waste disposable paper-cups will be used for the formation of graphene sheets by using KOH activation while using FeCl3 as a catalyst. The chemical activation of carbon-rich materials using KOH as the activating agent is a ground-breaking technique because of its lower activation temperature and higher yield [1]. Also, the obtained graphene will have a well-defined micropore size distribution and extremely high surface area. The graphene nanostructure can be used as a filler for thermoplastic polymers since it provides polymers of several functional characteristics, which are not accessible by using other types of fillers. For this research, the extracted graphene nanoparticles will be used as a filler in polypropylene to enhance its morphological, mechanical, and thermal properties. Thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) tests will be used to determine the thermal properties of the graphene reinforced polymer composites. Also, X-ray diffraction (XRD), FTIR, SEM, TEM, and tensile tests will be used to determine the mechanical, microscopic, and morphological property.es of GRPC. The results obtained from this research indicates that high yield and high-quality graphene materials can be synthesized by using biomass resources rather than conventional methods and can be used as a filler in polymers that could improve the mechanical and thermal performance when dispersed uniformly.

Morphological, Mechanical, and Thermal Properties of Polypropylene Reinforced with Graphene Nanoparticles Extracted from Paper Cups.

The Centers of Research Excellence in Science and Technology (CREST) program provides support to enhance the research capabilities of minority-serving institutions (MSI). Southern University and A&M College in Baton Rouge (SUBR) established its Next Generation Composites CREST Center (NextGenC3) by a unifying theme of multiscale and multifunctional composites and related technology development of advanced fiber reinforced polymer composites with capabilities to address varying technological needs and challenges in material applications. Dr. Jahan in this colloquia session will provide an introduction and overview of her projects focusing on food wastes and byproducts. Her team at SUBR provides educational and research training opportunities to traditionally underrepresented minority (URM) students in Science, Technology, Engineering, and Mathematics (STEM) disciplines by using enhanced non-structural functionalities of the bio-composites towards a potential replacement for their inorganic-based counterparts in automotive, aerospace industries, and researchable fuel cell applications.

Potential Multifunctional Composite using carbonized coconut shell particles

CREST Center for Dynamic Multiscale and Multimodal Brain Mapping over the Lifespan [D-MAP]

The relevance of indigenous Psychologies for understanding human experience. Who gets to decide?

Multimodal binding as mereological co-constituency

Much of our knowledge come from other people. In considering how argument quality and source reliability influences message persuasiveness, we conduct a comparison of the Elaboration Likelihood Model of Persuasion and the Bayesian Model of Argumentation, which are based on different assumptions. Participants were asked to judge a fictitious character’s degree of belief in a claim given evidence. To test competing predictions, we manipulate the character’s elaboration level, the argument’s quality, and the source’s reliability. The elaboration did not moderate the main effects of argument quality and source reliability, as they both were integral to the overall message strength in both high and low elaboration conditions. Bayesian predictions have better fit with the observed data, whilst ELM predictions did not align well. Overall, the BM is supported, but we discuss how this model could be further improvised while the ELM is contested


Comparing predictions from the Elaboration Likelihood Model and a Bayesian model of argumentation

Current theory suggests that the heterogeneity of metapopulation structure can help foster the diffusion of innovations to solve pressing issues including climate change adaptation and promoting public health. In this paper, we develop an agent-based model of the spread of adaptations in populations with such minority-majority metapopulation structure, where subpopulations have different preferences for social interactions (i.e., homophily) and, consequently, learn deferentially from their own group. In our simulations, minority-majority-structured populations with moderate degrees of in-group preference better spread and maintained an adaptation compared to populations with more equal-sized groups and weak homophily. Minority groups act as incubators for a novel adaptations, while majority groups act as reservoirs for the adaptation once it has spread widely. This suggests that population structure with in-group preference could promote the maintenance of novel adaptations, in the absence of out-group aversion.

Minority-group incubators and majority-group reservoirs for promoting the diffusion of climate change and public health adaptations

Humans are remarkably effective social learners, with several recent studies formalizing this capacity using computational models. 
However, previous research has often been limited to tasks where observer and demonstrator share the same reward function. In contrast, humans can learn from others who have different preferences, skills, or goals. To study social learning under individual differences, we introduce the socially correlated bandit, where participants have personalized rewards, which are correlated with but not identical to those of others. Social information can still be useful, but not when used verbatim.
We present a model of social generalization that integrates individual and social information into the generalization process, but assumes social information to be noisier and thus less informative. This model out-competes previous models, with it being the dominant strategy in evolutionary simulations.
Our findings expand on previous models of social learning, showing humans can integrate social information more flexibly than previously assumed.


Social learning with a grain of salt

Deep reinforcement learning (DRL) is capable of training agents that exceed human-levels of performance in multi-agent tasks. However, the behaviors exhibited by these agents are not guaranteed to be human-like or human-compatible. This poses a problem if the goal is to design agents capable of collaborating with humans or augmenting human actions in cooperative or team-based tasks. Indeed, recommender systems designed to augment human decision making need to not only recommend actions that align with the task goal, but which also maintain coordinative behaviors between agents. The current study simultaneously explored skill learning performance of human learners when working alongside different artificial agents (AAs) during a collaborative problem-solving task, as well as evaluated the effectiveness of the same AAs as action decision recommender systems to aid learning. The action decisions of the AAs were either modelled by a heuristic model based on human performance or by a deep neural network trained by reinforcement learning in self-play. In addition to evaluating skill learning performance, the current study also tested the congruence between the decisions of the AAs with actual decisions made by humans. Results demonstrate that the performance of humans was significantly worse when working alongside the DRL AA compared to the heuristic AA. Additionally, the action decisions participants made also showed less allignment with the recommendations made by the DRL AA.

Action decision congruence between human and deep reinforcement learning agents during a coordinated action task

People are known for their ability to learn probabilistic rewarding rules of the environment. However, the rules tested in most studies consisted of one feature dimension or a linear combination of multiple features. It is unclear how well people can learn non-linear combinatory rules where each single dimension is noninformative. In an adapted matching-pennies game, we asked participants to predict whether their opponent would choose head or tail next trial. Though the feature space for possible rules was small, including only two binary dimensions (i.e., the two players’ current choices), participants had striking failures in learning the probabilistic XOR rules (e.g., the opponent would have a probability of 0.8 to choose head if the two players had matching pennies and tail for unmatching pennies). The failure persisted in a series of experiments, even when the feature space was made explicit, after 320 trials of learning, and for different cover stories.

People fail to learn probabilistic XOR rules even in a small feature space

Active learning techniques enhance understanding and engagement in the learner, but these techniques can be difficult to teach to instructors. The pedagogy of active learning typically either describes abstract concepts or it relies heavily on case studies of what other instructors have created. Because it can be difficult for interested instructors to put theory into practice without a concrete structure, active learning techniques can be hard to implement in the classroom. Behaviorism, cognitivism, constructivism, and connectivism can be used to describe ideas about how learning is occurring, but they do not accurately categorize commonly used teaching activities, which instead often contain elements of multiple learning theories. I propose a novel functional categorization of active learning techniques that is both brain-based and student-centered, placing emphasis on the knowledge journey. This work describes these seven evidence-based “knowledge catalysts” as a new user-friendly framework for both understanding, using, and teaching active learning techniques.

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Center for Energy & Environmental Sustainability (CEES)

Morphological, Mechanical, and Thermal Properties of Polypropylene Reinforced with Graphene Nanoparticles Extracted from Paper Cups.

Potential Multifunctional Composite using carbonized coconut shell particles

CREST Center for Dynamic Multiscale and Multimodal Brain Mapping over the Lifespan [D-MAP]

The relevance of indigenous Psychologies for understanding human experience. Who gets to decide?

Multimodal binding as mereological co-constituency

Comparing predictions from the Elaboration Likelihood Model and a Bayesian model of argumentation

Minority-group incubators and majority-group reservoirs for promoting the diffusion of climate change and public health adaptations

Social learning with a grain of salt

Action decision congruence between human and deep reinforcement learning agents during a coordinated action task

People fail to learn probabilistic XOR rules even in a small feature space

Knowledge Catalysts: A Structure to Promote Incorporation of Active Learning Techniques

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