Missouri University of Science and Technology

NSF Awards · FY 2024 · 2024-09

2437368 (Burken). This grant provides partial support for US student participation in the International Phytotechnologies Scholars Program to be held in October 2024 at the University of Calicut, Kerala, India. The conference is hosted by the International Phytotechnology Society (IPS). The IPS conference has the goal of advancing knowledge in areas protecting human health and advancing sustainable solutions that can be broadly applicable, with a particular focus on increasing knowledge of the broader roles of phytotechnologies in the concurrent protection of public health, risk assessment, and the improvement of ecosystem function. Participation in the conference will provide students with professional skills and development opportunities and the chance to network with international professionals working in the field of phytotechnologies. The conference will directly support student training, international engagement, mentorship, and education in phytotechnologies and sustainable technologies as strategies for environmental remediation, management, and stewardship. This grant will provide partial coverage of travel and lodging for an expected 10 - 12 selected scholars to attend the conference. The selected students will travel to India and will directly interact with international leaders in public health protection to learn scientifically and grow professionally. At the conference, the students will make a presentation and meet leaders of the profession. The students will be provided with mentoring and will be charged to meet specific learning objectives, which include not only scientific objectives but also cultural and professional development objectives. The students' presentations will be evaluated by a minimum of two senior scholars. The student selection process will invite applicants in groups underserved in STEM fields. The selection committee is a diverse group of accomplished scholars from multiple universities that has interacted with past conferences and student programs. All selection committee members will be invited to the conference to offer talks and engage with the students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The acquisition of the Quantum Design P960 Helium Reliquefier System by Missouri S&T, in collaboration with the University of Missouri Columbia and Washington University, significantly upgrades the existing Physical Property Measurement System (PPMS) that requires liquid helium for operation. This system significantly mitigates the regional challenges of securing liquid helium, crucial for advanced studies across a spectrum of fields including quantum materials, composite materials, and battery materials. It also ensures a steady supply by recapturing and recondensing helium gas during cryogenic experiments. As a result, it reduces operational costs and supports ongoing scientific endeavors. Additionally, the enhanced PPMS serves as a vital educational tool, demonstrating physical property measurements to K-12 and college students. This sustainable approach to resource management highlights the dedication of researchers to conserving critical scientific resources, ensuring ongoing and uninterrupted research and educational activities. The PPMS, operating within a temperature range of 2 to 400 K and in magnetic fields up to 9 T, supports a broad array of research crucial for scientific and technological advancements. This includes exploring anomalous Hall effects in topological superconductors essential for quantum computing, investigating magneto-transport in charge-doped Mott insulators, and identifying new materials like spin ice that may revolutionize magnetic semiconductor technologies. Studies utilizing the enhanced PPMS also focus on understanding dipole-dipole interactions in Azines, examining thermal behavior in diboride ceramics to uncover phonon and electron dynamics, and conducting detailed assessments of electrode materials to enhance thin film and doping techniques in battery research. These efforts collectively contribute to a deeper understanding of material properties and their applications in various technology domains. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Improving representation learning is a key challenge for improved machine learning models, particularly for online and real-time applications. Current machine learning methods are expensive and subject to catastrophic forgetting by overwriting previously learned information. Adaptive Resonance Theory (ART) is designed to avoid this overwriting, but it accomplishes this by combining match-based learning with a reset mechanism. This combination is effective at avoiding overwriting at the cost of missing the representation learning capabilities common in gradient approaches such as deep learning. Intellectual Merit: This project seeks to combine the advantages of match- and error-based learning by using first principles in combined design, as opposed to ad-hoc hybrids. It will investigate the potential for overcoming scalability and credit assignment problems entailed in hybrid approaches to date. It also seeks to improve explainability of ART by appropriate design of the resulting templates formed during training. It will also clarify the difference between catastrophic forgetting due to overwriting correctly learned information, versus apparent but valid performance degradation due to new information that was not represented during previous training. The goal is to solve the former and learn from the latter. Broader Impacts: The project will produce technological broader impacts of more cost-effective and trustworthy machine learning. The resulting systems will be less brittle and more explainable. They will not need an artificial freezing of learning capability but will be true lifelong learning machines. Demographic broader impacts will include mentoring for prospective researchers and undergraduates. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Non-technical description: Fault-tolerant topological quantum information technology promises a breakthrough in the fidelity issue in various nano-scale quantum devices, and the Majorana fluid plays a central role in this research effort. While the Majorana fluid emerging in the nanodevices can be used to realize topological quantum technology, the nature of observed signatures in such devices remains controversial. This LEAPS-MPS project focuses on (i) investigating the single-crystal topological superconductor that intrinsically harbors the Majorana surface fluid and (ii) incorporating relevant quantum materials research into various education activities. In particular, the project team is investigating the fundamental properties of topological superconductor candidates by measurements of London penetration depth and the superfluid density down to near absolute zero, which provides key information for the mechanism of topological superconductivity. The research topics are directly incorporated into education by offering research opportunities to undergraduate students and various outreach activities for K-12 students. The PI collaborates with Missouri S&T’s Kummer Center for STEM Education to host summer STEM events for area students. The PI also promotes quantum materials research to undergraduate students in the historically minority-serving Lincoln University of Missouri and expands the existing partnership with institutions and organizations in Missouri by incorporating quantum materials research and quantum computing science into various education activities. The LEAPS-MPS project grows the Missouri S&T's influence by encouraging entry into STEM education and careers. Technical description: The elusive Majorana fluid emerges on certain surfaces of a bulk topological superconductor, and the symmetry of its superconducting energy gap determines the Majorana-harboring surface. While there are a number of topological superconductor candidates, the gap symmetry in the proposed topological superconductors remains controversial. In this project, the research team will synthesize single crystals of various topological superconductor candidates and employ a low-millikelvin radio-frequency self-oscillator technique to investigate the superconducting energy gap in proposed topological superconductor candidates. The ultimate goal of this project is to confirm and identify the topological superconducting phase in the proposed materials. The rf-technique is implemented in a commercial dilution refrigerator and determines the temperature- and field-dependent London penetration depth at low-millikelvin temperature ranges, from which the superfluid density is subsequently determined. The possible symmetry of the superconducting energy gap is deduced from the measured penetration depth. Potential ambiguity in the gap symmetry due to the presence of disorder in as-grown single crystal samples is eliminated by introducing controlled disorders with various irradiation methods including gamma-ray and neutron. The irradiation is performed at the research reactor at Missouri S&T. Successful completion of this project enables highly efficient and focused investigation of the Majorana fluid. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

Modern scientific experiments and simulations generate enormous quantities of data. As a result, scientists in many fields need to process large scale datasets using a range of data analytics tools. Many such tools conduct computations that are data-intensive, which mean that they perform operations on a large quantity of data elements per unit time (rather than few data operations per unit time). These computations are typically memory-bound, implying that there is a very high ratio of memory operations to compute operations, and in this case, insufficient memory bandwidth (measured as the number of memory operations that can be carried out in a fixed time interval) creates a bottleneck. This means that multiple processors cannot access the data as quickly as they would like and so this limits the utilization of the processors which decreases performance. To address the above challenges, this project focuses on high performance data analytics by leveraging Processing-in-Memory (PIM) systems by performing data analytics inside memory chips. The PIM paradigm enables a new opportunity for designing efficient algorithms that can address these modern scientific datasets. With the advent of PIM hardware by several vendors, there is an opportunity to exploit real hardware and study the trade-offs of using this new paradigm for processing scientific datasets. PIM addresses the high energy and data movement costs between the CPU and memory by placing computational power near memory, thus limiting the need to perform all computations on the CPU. Moreover, programming real PIM systems is challenging for scientists due to complicated interfaces. This project will advance cyberinfrastructure research and will design new similarity search algorithms for point and polygon datasets for scientific fields such as geoscience, pathology, astronomy, and solar physics. Similarity searches find similar objects to a query object and provide the foundations for many data analytics tools used by scientists. By leveraging the proposed techniques, researchers in many science and engineering fields will be able to easily adapt their scientific applications to PIM hardware. This project will provide training and research projects for graduate students. Educational materials related to the research objectives will be developed, deployed in the classroom, and then disseminated through educational workshops presented at parallel computing venues.  This project will examine efficient indexing and similarity searches on high-dimensional points and polygons that are common to many scientific workloads which require optimizations at different levels, including: I/O, algorithmic innovations, and optimized communication patterns. The overarching goal is to advance new algorithmic designs that exploit the changing landscape of memory hardware. PIM-aware data structures including the R-tree, KD-tree, locality sensitive hashing, and product quantization will be designed and implemented. Range searches, similarity searches, and K-Nearest Neighbor searches for UPMEM PIM systems will be developed. For performance modeling of core algorithms, the project will leverage Roofline and Iso-Efficiency analysis on the PIM system. From the programmability and productivity perspective, the projectalso proposes to develop a communication-efficient library and a MapReduce-like programming interface for search-and-refine workloads. Broader impacts of this project include training graduate students, the dissemination of open source software, and the development, evaluation, and dissemination of pedagogic materials that train students to use PIM architectures. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

The envisioned era of “smart living” aims to improve human quality of life and experience, leading to a better and safer society, with the help of smart sensors and devices, the Internet of Things (IoT), and cyber-physical systems (CPS) coupled with advanced data analytics, artificial intelligence (AI) and machine learning (ML) techniques. The voluminous data collected from smart living applications (e.g., smart buildings/cities, smart energy, smart transportation, smart manufacturing, smart health, smart agriculture, disaster response) are vulnerable to a wide variety of security threats and privacy/trust breaches, thwarting the accuracy of decision making and operational impacts on which the modern society depends. Attack (or anomaly) detection in smart living CPS poses unique challenges since the collected data are also affected by the behavioral randomness of human users. Privacy and trust issues in smart societies are exacerbated owing to socio-economic-cultural differences. In addition, one needs to consider the tradeoff between privacy, safety, and security which are important at a practical level to the community members. Recognizing the research challenges and opportunities in smart and connected communities, in recent years the NSF and the Japan Science and Technology (JST) Agency have established joint funding programs to support collaborative cutting-edge research and development in smart living. This proposal aims to continue and advance that dialog, by requesting funds for US researchers to participate in the NSF-JST Workshop on Secure and Resilient Smart Living CPS to be held in Osaka, Japan on July 2-3, 2024. The outcome of the workshop will be to catalyze mutually beneficial areas of further collaborations between the researchers in the US and Japan in the areas of security, privacy, trust, resilience, safety, dependability, and robustness of smart living CPS, potentially leading to joint funding opportunities. Such bilateral cooperation will help establish stronger US-Japan strategic alliance in research, education, technology innovation, entrepreneurship, and workforce development that can benefit the citizens and society at large in both countries. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-06

Population dynamics are the changes in age structure and density in a population over time. These changes are driven by the genes of the organisms, their interactions with each other, and their interactions with a fluctuating biotic and abiotic environment. Some organisms communicate with each other about their local environment, and this communication may influence population dynamics. This project uses the small roundworm Caenorhabditis elegans to study communication between animals in populations through chemical signals. C. elegans is a powerful genetic model organism with ideal characteristics for studying population dynamics (e.g., a short lifecycle of three days, can be inexpensively grown in the lab in large numbers). Chemical signals help C. elegans to survive famines by sending some of them into diapause (i.e. a kind of hibernation), but many questions remain about how communication at the individual level corresponds to the properties of entire populations. Studying how populations react to changes in their environment is an important step to improve model forecasting of populations for pest control or conservation of wild species. Another important goal of the project is to promote a diverse and strong future science work force by enhancing research opportunities for undergraduate students in the researcher’s rural community through paid hands-on experience in the lab. In addition, the project will include outreach to local schools and colleges providing opportunities for students to engage in hypothesis-driven research through computer modeling of population dynamics. This project aims to understand how pheromone signaling and the dauer diapause stage of C. elegans influence population dynamics in different environmental conditions. Two recently established research tools will be used to (1) determine the effect of dauer pheromone communication on C. elegans population dynamics in a laboratory system, and (2) define the impact of responsiveness to dauer pheromone and starvation on C. elegans population dynamics in a realistic agent-based computer model. Mutant strains and wild isolates with variations in genes that influence pheromone signaling will be used to rigorously test the function of pheromones for population dynamics. Laboratory populations of these strains/isolates and simulated populations with fine-tuned changes in the pheromone signaling phenotype will be analyzed for at least 30 generations under continuous high bacterial food availability and compared to populations with 10-day or 30-day starvation periods. These experiments will determine how pheromonal dauer communication impacts fitness under different environmental conditions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.